Predictive weed map and material application machine control

ABSTRACT

A predictive map is obtained by an agricultural material application system. The predictive map maps predictive weed values at different geographic locations in a field. A geographic position sensor detects a geographic locations of an agricultural material application machine at the field. A control system generates a control signal to control the agricultural material application machine based on the geographic locations of the agricultural material application machine and the predictive map.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of and claims priorityof U.S. patent application Ser. No. 17/067,383, filed Oct. 9, 2020,which is a continuation-in-part of and claims priority of U.S. patentapplication Ser. No. 16/783,475, filed Feb. 6, 2020, and Ser. No.16/783,511, filed Feb. 6, 2020, the present application is acontinuation-in-part of and claims priority of U.S. patent applicationSer. No. 17/066,444, filed Oct. 8, 2020, which is a continuation-in partof and claims priority of U.S. patent application Ser. No. 16/783,475,filed Feb. 6, 2020, and Ser. No. 16/783,511, filed Feb. 6, 2020. Thecontents of all of the above applications are hereby incorporated byreference in their entirety.

FIELD OF THE DESCRIPTION

The present description relates to agriculture. More specifically, thepresent description relates to agricultural machines and operationswhich deliver material to a worksite.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines apply material, such as fluid or solidmaterial, to a field. For instance, some machines, such as sprayers ordry spreaders, can deliver fluid or solid material, such as fertilizer,herbicide, pesticide, as well as variety of other materials to a field.Some machines, such as agricultural planting machines, can delivermaterial such as seeds, as well as other material, such as liquid orsolid material, for instance, fertilizer.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

A predictive map is obtained by an agricultural material applicationsystem. The predictive map maps predictive weed values at differentgeographic locations in a field. A geographic position sensor detects ageographic locations of an agricultural material application machine atthe field. A control system generates a control signal to control theagricultural material application machine based on the geographiclocations of the agricultural material application machine and thepredictive map.

Example 1 is an agricultural material application system comprising:

a geographic position sensor that detects a geographic location of amobile material application machine at a field;

a control system that:

receives a predictive map that maps predictive weed values to differentgeographic locations in the field; and

generates a control signal to control a controllable subsystem of themobile material application machine based on the geographic location ofthe mobile material application machine and the predictive map.

Example 2 is the agricultural material application system of any or allprevious examples and further comprising:

an in-situ sensor that detects a weed value corresponding to ageographic location;

a predictive model generator that:

receives an information map that maps values of a characteristiccorresponding to different geographic locations in the field; and

generates a predictive model indicative of a relationship between valuesof the characteristic and weed values based on the weed value detectedby the in-situ sensor corresponding to the geographic location and avalue of the characteristic in the information map corresponding to thegeographic location; and

a predictive map generator that generates, as the predictive map, afunctional predictive map of the field that maps predictive weed valuesto the different geographic locations in the field based on the valuesof the characteristic in the information map and based on the predictivemodel.

Example 3 is the agricultural material application system of any or allprevious examples wherein the weed value is indicative of one or more ofweed presence, weed type, weed size, and weed intensity.

Example 4 is the agricultural material application system of any or allprevious examples, wherein the information map comprises a vegetativeindex map that maps vegetative index values to the different geographiclocations in the field;

wherein the predictive model generator generates, as the predictivemodel, a predictive weed model that models a relationship betweenvegetative index values and weed values based on the weed value detectedby the in-situ sensor corresponding to the geographic location and avegetative index value in the vegetative index map at the geographiclocation to which the detected weed values corresponds, the predictiveweed model being configured to receive a vegetative index value as amodel input and generate a predictive weed value as a model output; and

wherein the predictive map generator generates, as the functionalpredictive map, a functional predictive weed map that maps predictiveweed values to the different geographic locations in the field based onthe vegetative index values in the information map and based on thepredictive weed model.

Example 5 is the agricultural material application system of any or allprevious examples, wherein the information map comprises an optical mapthat maps optical characteristic values to the different geographiclocations in the field;

wherein the predictive model generator generates, as the predictivemodel, a predictive weed model that models a relationship betweenoptical characteristic values and weed values based on the weed valuedetected by the in-situ sensor corresponding to the geographic locationand an optical characteristic value in the optical map at the geographiclocation to which the detected weed values corresponds, the predictiveweed model being configured to receive an optical characteristic valueas a model input and generate a predictive weed value as a model output;and

wherein the predictive map generator generates, as the functionalpredictive map, a functional predictive weed map that maps predictiveweed values to the different geographic locations in the field based onthe optical characteristic values in the information map and based onthe predictive weed model.

Example 6 is the agricultural material application system of any or allprevious examples, wherein the information map comprises a weed map thatmaps weed values to the different geographic locations in the field;

wherein the predictive model generator generates, as the predictivemodel, a predictive weed model that models a relationship between weedvalues and weed values based on the weed value detected by the in-situsensor corresponding to the geographic location and a weed value in theweed map at the geographic location to which the detected weed valuescorresponds, the predictive weed model being configured to receive aweed value as a model input and generate a predictive weed value as amodel output; and

wherein the predictive map generator generates, as the functionalpredictive map, a functional predictive weed map that maps predictiveweed values to the different geographic locations in the field based onthe weed values in the information map and based on the predictive weedmodel.

Example 7 is the agricultural material application system of any or allprevious examples, wherein the information map comprises two or moreinformation maps, each of the two or more information maps mappingvalues of a respective characteristic to the different geographiclocations in the field;

wherein the predictive model generator generates, as the predictivemodel, a predictive weed model indicative of a relationship betweenvalues of the two or more respective characteristics and weed valuesbased on the weed value detected by the in-situ sensor corresponding tothe geographic location and values of the two or more respectivecharacteristics in the two or more information maps corresponding to thegeographic location, the predictive weed model being configured toreceives a value of each of the two or more respective characteristicsas model inputs and generate a predictive weed value as a model output;and

wherein the predictive map generator generates, as the functionalpredictive map, a functional predictive weed map that maps predictiveweed values to the different geographic locations in the field based onthe values of the two more respective characteristics in the two or moreinformation maps and the predictive weed model.

Example 8 is the agricultural material application system of any or allprevious examples, wherein the controllable subsystem comprises amaterial application actuator and wherein the control signal controlsthe material application actuator to increase an amount of materialapplied by the material application machine based on the functionalpredictive weed map.

Example 9 is the agricultural material application system of any or allprevious examples, wherein the controllable subsystem comprises amaterial application actuator and wherein the control signal controlsthe material application actuator to decrease an amount of materialapplied by the material application machine based on the functionalpredictive weed map.

Example 10 is the agricultural material application system of any or allprevious examples, wherein the controllable subsystem comprises amaterial application actuator and wherein the control signal controlsthe material application actuator to deactivate or activate a componentof the material application machine based on the functional predictiveweed map.

Example 11 is a method of controlling a mobile agricultural materialapplication machine comprising:

receiving a predictive map of a field that maps predictive weed valuescorresponding to different geographic locations in the field;

detecting a geographic location of the mobile agricultural materialapplication machine at the field;

controlling a controllable subsystem of the mobile agricultural materialapplication machine based on the geographic location of the mobileagricultural material application machine and the predictive map.

Example 12 is the method of any or all previous examples and furthercomprising:

receiving an information map that maps values of a characteristic todifferent geographic locations in a field;

obtaining in-situ sensor data indicative of a weed value correspondingto a geographic location at the field;

generating a predictive weed model indicative of a relationship betweenvalues of the characteristic and weed values; and

generating, as the predictive map, a functional predictive weed map ofthe field, that maps predictive weed values to the different geographiclocations in the field based on the values of the characteristic in theinformation map and the predictive model.

Example 13 is the method of any or all previous examples, whereincontrolling a controllable subsystem comprises controlling a materialapplication actuator of the mobile agricultural material applicationmachine based on the geographic location of the mobile agriculturalmaterial application machine and the functional predictive weed map.

Example 14 is the method of any or all previous examples, whereincontrolling the material application actuator comprises controlling thematerial application actuator of the mobile agricultural materialapplication machine to adjust a rate at which material is applied to thefield based on the geographic location of the mobile agriculturalmaterial application machine and the functional predictive weed map.

Example 15 is the method of any or all previous examples, whereincontrolling the material application actuator comprises controlling thematerial application actuator of the mobile agricultural materialapplication machine to activate or deactivate a component of the mobileagricultural material application machine based on the geographiclocation of the mobile agricultural material application machine and thefunctional predictive weed map.

Example 16 is a mobile agricultural material application machine,comprising:

a geographic position sensor that detects a geographic location of themobile agricultural material application machine at a field; and

a control system that:

receives a predictive map that maps predictive weed values to differentgeographic locations in the field; and

generates a control signal based on the geographic location of themobile agricultural material application machine at the field and thepredictive map.

Example 17 is the mobile agricultural material application machine ofany or all previous examples and further comprising:

a communication system that receives an information map that maps valuesof a characteristic to different geographic locations in the field;

an in-situ sensor that detects a weed value corresponding to thegeographic location;

a predictive model generator that generates a predictive weed modelindicative of a relationship between values of the characteristic andweed values based on the weed value detected by the in-situ sensorcorresponding to the geographic location and a value of thecharacteristic in the information map at the geographic location; and

a predictive map generator that generates, as the predictive map, afunctional predictive weed map of the field that maps predictive weedvalues to the different geographic locations in the field based on thevalues of the characteristic in the information map at those differentgeographic locations and based on the predictive weed model.

Example 18 is the mobile agricultural machine of any or all previousexamples, wherein the control system generates the control signal tocontrol a controllable subsystem of the mobile agricultural materialapplication machine.

Example 19 is the mobile agricultural material application machine ofany or all previous examples, wherein the control system generates thecontrol signal to control an actuator that is controllably actuatable toadjust a rate at which material is applied to the field.

Example 20 is the mobile agricultural material application machine ofany or all previous examples, wherein the control system generates thecontrol signal to control an actuator to activate or deactivate acomponent of the mobile agricultural material application machine.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial block diagram showing one exampleof a mobile agricultural material application machine as a mobileagricultural planting machine.

FIG. 2 is a side view showing one example of a row unit of the mobileagricultural planting machine shown in FIG. 1

FIG. 3 is a side view showing another example of a row unit of themobile agricultural planting machine shown in FIG. 1.

FIG. 4 shows an example of a seed metering system.

FIG. 5 shows an example of a seed delivery system that can be used witha seed metering system.

FIG. 6 shows another example of a seed delivery system that can be usedwith a seed metering system.

FIG. 7 is a partial pictorial, partial block diagram showing one exampleof a mobile agricultural material application machine as a mobileagricultural sprayer.

FIG. 8 is a partial pictorial, partial block diagram showing one exampleof a mobile agricultural material application machine as a mobileagricultural sprayer.

FIG. 9 shows an example of a material delivery machine.

FIG. 10 is a block diagram showing some portions of an agriculturalmaterial application system, including a mobile agricultural materialapplication machine, in more detail, according to some examples of thepresent disclosure.

FIG. 11 is a block diagram showing one example of a predictive modelgenerator and predictive map generator.

FIG. 12 is a block diagram showing one example of a predictive modelgenerator and predictive map generator.

FIG. 13 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 14 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIGS. 15A-15B (collectively referred to herein as FIG. 15) show a flowdiagram illustrating one example of operation of an agriculturalmaterial application system in generating a map.

FIG. 16 is a block diagram showing one example of a logistics system inmore detail.

FIG. 17 is a flow diagram illustrating one example of operation of anagricultural material application system in controlling a materialapplication operation.

FIG. 18 is a block diagram showing one example of a mobile materialapplication machine in communication with a remote server environment.

FIGS. 19-21 show examples of mobile devices that can be used in anagricultural material application system.

FIG. 22 is a block diagram showing one example of a computingenvironment that can be used in an agricultural material applicationsystem.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, and/or steps described with respect toone example may be combined with the features, components, and/or stepsdescribed with respect to other examples of the present disclosure.

In some examples, the present description relates to using in-situ datataken concurrently with an operation, such as an agricultural materialapplication operation, in combination with prior or predicted data, suchas prior or predicted data represented in a map, to generate apredictive model and a predictive map. In some examples, the predictivemap can be used to control a mobile machine, such as a mobileagricultural material application machine or a material deliverymachine, or both.

During an agricultural material application operation material, such asseed, fertilizer, herbicide, pesticide, etc., is delivered to the field.The application of material can be controlled, such as by an operator oruser, or by an automated control system, or both. It may be desirable tocontrollably (e.g., variably) apply material, based on thecharacteristics of the field. For example, it may be desirable to varythe amount of material applied at a given locations, based on thenutrient levels at those locations. For instance, some locations of thefield may have adequate or near adequate nutrient levels, such that nofertilizer or relatively less fertilizer need be applied. In otherexamples, some locations of the field may have nutrient levels thatrequire the application of more material than expected. In otherexamples, it may be desirable to vary the amount of material applied atgiven locations, based on the weed characteristics at those locations.For instance, herbicide may not be required at given location due tolack of weeds at those location, or additional herbicide may be neededwhere the weeds are particularly intense.

Applying material as needed based on the field conditions at the time ofthe operation, as opposed to a blanket application or a prescribedapplication determined ahead of the operation in the field, may savecost, may reduce environmental impact, as well as result in moreeffective material use, which may result in higher yields.

Some current systems may include sensors that detect characteristicsindicative of nutrient levels of the field which can be used in thecontrol of material application. However, such systems often includelatency, such as due to the sensor feedback delay or due to the machinecontrol delay, which may result in suboptimal material application.

The present description thus relates to a system that can predictcharacteristic values, such as nutrient values or weed values, or both,at different locations across the worksite, such that a mobileagricultural material application machine can be proactively controlled.

In some examples, it may be desirable to know when a materialapplication machine will run out of material. As the operator or user orcontrol system may vary the application throughout the operation it canbe difficult to know, a priori, where the machine will run out ofmaterial.

Knowing when and where the machine will run out of material can beuseful in planning logistics of the material application operation, suchas scheduling or meeting a material delivery vehicle. Efficientscheduling can reduce downtime, as well as provide various otherbenefits.

The present description thus relates to a system that can predictmaterial consumption values at different locations across the worksite,such that the material application operation can be proactivelycontrolled.

In one example, the present description relates to obtaining aninformation map, such as a soil property map. A soil property mapillustratively maps soil property values (which may be indicative ofsoil type, soil moisture, soil structure, soil salinity, soil pH, soilorganic matter, soil contaminant concentration, soil nutrient levels, aswell as various other soil properties) across different geographiclocations in a field of interest. The soil property maps thus providegeo-referenced soil properties across a field of interest. Soil type canrefer to taxonomic units in soil science, wherein each soil typeincludes defined sets of shared properties. Soil types can include, forexample, sandy soil, clay soil, silt soil, peat soil, chalk soil, loamsoil, and various other soil types. Soil moisture can refer to theamount of water that is held or otherwise contained in the soil. Soilmoisture can also be referred to as soil wetness. Soil structure canrefer to the arrangement of solid parts of the soil and the pore spacelocated between the solid parts of the soil. Soil structure can includethe way in which individual particles, such as individual particles ofsand, silt, and clay, are assembled. Soil structure can be described interms of grade (degree of aggregation), class (average size ofaggregates), and form (types of aggregates), as well as a variety ofother descriptions. Soil salinity refers to the amount (e.g.,concentration) of salt in the soil. Soil nutrient levels refers to theamounts (e.g., concentrations) of various nutrients of the soil, such asnitrogen. These are merely examples. Various other characteristics andproperties of the soil can be mapped as soil property values on a soilproperty map. The soil property map can be derived in a variety of ways,such as from sensor readings during previous operations at the field ofinterest, from surveys of the field, such as soil sampling surveys, aswell as surveys by aerial machines (e.g., satellites, drones, etc.) thatincludes sensors that capture sensor information of the field. The soilproperty map can be generated based on data from remote sources, such asthird-party service providers or government agencies, for instance, theUSDA Natural Resources Conservation Service (NRCS), the United StatesGeological Survey (USGS), as well as from various other remote sources.These are merely some examples. The soil property map can be generatedin a variety of other ways.

In one example, the present description relates to obtaining aninformation map, such as a yield map. A yield map illustratively mapsyield values across different geographic locations in a field ofinterest. The yield map may be based on sensor readings taken during anaerial survey of the field of interest or during a previous operation onthe field of interest, or derived from other values, such as vegetativeindex values. In some examples, the yield map may be a historical yieldmap that includes historical yield values from a previous harvestingoperation, such as the harvesting operation from a prior year or a priorseason. These are merely some examples. The yield map can be generatedin a variety of other ways.

In one example, the present description relates to obtaining aninformation map, such as a residue map. A residue map illustrativelymaps residue values (which may be indicative of residue amount andresidue distribution) across different geographic locations in a fieldof interest. Residue illustratively refers to vegetation residue, suchas remaining vegetation material at the field of interest, such asremaining crop material, as well as material of other plants, such asweeds. The residue map may be derived from sensor readings during aprevious operation at the field. For example, the machine performing theprevious operation may be outfitted with sensors that detect residuevalues at different geographic locations in the field. The residue mapmay be derived from sensor readings from sensors on aerial machine(e.g., satellites, drones, etc.) that survey the field of interest. Thesensors may read one or more bands of electromagnetic radiationreflected from the residue material at the field. These are merely someexamples. The residue map can be generated in a variety of other ways.

In one example, the present description relates to obtaining aninformation map, such as a constituents map. A constituents mapillustratively maps constituent values (which may be indicative ofconstituent levels (e.g., concentrations) of constituents, such as,sugar, starch, fiber, water/moisture, etc., of crop plants) acrossdifferent geographic locations in a field of interest. The constituentmap may be derived from sensor readings during a previous operation atthe field. The constituent map may be derived from sensor readings fromsensors on aerial machine (e.g., satellites, drones, etc.) that surveythe field of interest. The sensors may read one or more bands ofelectromagnetic radiation reflected from the residue material at thefield. These are merely some examples. The constituent map can begenerated in a variety of other ways.

In one example, the present description relates to obtaining aninformation map, such as a topographic map. A topographic mapillustratively maps topographic characteristic values across differentgeographic locations in a field of interest, such as elevations of theground across different geographic locations in a field of interest.Since ground slope is indicative of a change in elevation, having two ormore elevation values allows for calculation of slope across the areashaving known elevation values. Greater granularity of slope can beaccomplished by having more areas with known elevation values. As anagricultural machine travels across the terrain in known directions, thepitch and roll of the agricultural machine can be determined based onthe slope of the ground (i.e., areas of changing elevation). Topographiccharacteristics, when referred to below, can include, but are notlimited to, the elevation, slope (e.g., including the machineorientation relative to the slope), and ground profile (e.g.,roughness). The topographic map can be derived from sensor readingstaken during a previous operation on the field of interest or from anaerial survey of the field (such as a plane, drone, or satelliteequipped with lidar or other distance measuring devices). In someexamples, the topographic map can be obtained from third parties. Theseare merely some examples. The topographic map can be generated in avariety of other ways.

In one example, the present description relates to obtaining aninformation map, such as a seeding map. A seeding map illustrativelymaps values of seeding characteristics (e.g., seed location, seedspacing, seed population, seed genotype, etc.) across differentgeographic locations in a field of interest. The seeding map may bederived from control signals used by a planting machine when plantingseeds or from sensors on the planting machine that confirm that a seedwas metered or planted. The seeding map can be generated based on aprescriptive seeding map that was used in the control of a plantingoperation. These are merely some examples. The seeding map can begenerated in a variety of other ways.

In one example, the present description relates to obtaining aninformation map such as a vegetative index map. A vegetative index mapillustratively maps georeferenced vegetative index values (which may beindicative of vegetative growth or plant health) across differentgeographic locations in a field of interest. One example of a vegetiveindex includes a normalized difference vegetation index (NDVI). Thereare many other vegetative indices that are within the scope of thepresent disclosure. In some examples, a vegetive index map be derivedfrom sensor readings of one or more bands of electromagnetic radiationreflected by the plants.

Without limitations, these bands may be in the microwave, infrared,visible or ultraviolet portions of the electromagnetic spectrum. Avegetative index map can be used to identify the presence and locationof vegetation. In some examples, these maps enable vegetation to beidentified and georeferenced in the presence of bare soil, crop residue,or other plants, including crop or other weeds. The sensor readings canbe taken at various times, such as during satellite observation of thefield of interest, a fly over operation (e.g., manned or unmanned aerialvehicles), sensor readings during a prior operation) at the field ofinterest, as well as during a human scouting operation. These are merelysome examples. The vegetative index map can be generated in a variety ofother ways.

In one example, the present description relates to obtaining a map, suchas an optical map. An optical map illustratively maps electromagneticradiation values (or optical characteristic values) across differentgeographic locations in a field of interest. Electromagnetic radiationvalues can be from across the electromagnetic spectrum. This disclosureuses electromagnetic radiation values from infrared, visible light andultraviolet portions of the electromagnetic spectrum as examples onlyand other portions of the spectrum are also envisioned. An optical mapmay map datapoints by wavelength (e.g., a vegetative index). In otherexamples, an optical map identifies textures, patterns, color, shape, orother relations of data points. Textures, patterns, or other relationsof data points can be indicative of presence or identification ofvegetation on the field (e.g., crops, weeds, plant matter, such asresidue, etc.). Additionally, or alternatively, an optical map mayidentify the presence of standing water or wet spots on the field. Theoptical map can be derived using satellite images, optical sensors onflying vehicles such as UAVS, or optical sensors on a ground-basedsystem, such as another machine operating in the field prior to thecurrent operation. In some examples, optical maps may mapthree-dimensional values as well such as vegetation height when a stereocamera or lidar system is used to generate the map. These are merelysome examples. The optical map can be generated in a variety of otherways.

In one example, the present description relates to obtaining aninformation map, such as a weed map. A weed map illustratively maps weedvalues (which may be indicative of weed location, weed presence, weedtype, and weed intensity (e.g., density)) across different geographiclocations in a field of interest. The weed map may be derived fromsensor readings during a previous operation at the field. The weed mapmay be derived from sensor readings from sensors on aerial machine(e.g., satellites, drones, etc.) that survey the field of interest. Thesensors may read one or more bands of electromagnetic radiationreflected from the weed material at the field. The weed map may bederived from various other data, such as optical characteristic data orvegetative index data of the field of interest. These are merely someexamples. The weed map can be generated in a variety of other ways.

In one example, the present description relates to obtaining aninformation map, such as a contamination map. A contamination mapillustratively maps contamination values (which may be indicative ofpest presence, pest type, pest intensity (e.g., population), diseasepresence, disease type, and disease intensity (e.g., prevalence)) acrossdifferent geographic locations in a field of interest. The contaminationmap may be derived from sensor readings during a previous operation atthe field. The contamination map may be derived from sensor readingsfrom sensors on aerial machine (e.g., satellites, drones, etc.) thatsurvey the field of interest. The sensors may read one or more bands ofelectromagnetic radiation reflected from the vegetation material (orfrom the contaminants) at the field. The contamination map may bederived from various other data, such as optical characteristic data orvegetative index data of the field of interest. These are merely someexamples. The contamination map can be generated in a variety of otherways.

In other examples, one or more other types of information maps can beobtained. The various other types of information maps illustratively mapvalues of various other characteristics across different geographiclocations in a field of interest.

The present discussion proceeds, in some examples, with respect tosystems that obtain one or more information maps of a worksite (e.g.,field) and also use an in-situ sensor to detect a characteristic. Thesystems generate a model that models a relationship between the valueson the one or more obtained maps and the output values from the in-situsensor. The model is used to generate a predictive map that predicts,for example, values of the characteristic detected by the in-situ sensorto different geographic locations in the worksite. The predictive map,generated during an operation, can be presented to an operator or otheruser or can be used in automatically controlling a mobile machine, suchas a mobile agricultural material application machine or a materialdelivery machine, or both, during a material application operation.

FIG. 1 shows one example of a mobile agricultural material applicationmachine 100 as a mobile agricultural planting machine 100-1 thatincludes a towing vehicle 10 and a planting implement 101. FIG. 1 alsoillustrates that mobile agricultural planting machine 100-1 can includeone or more in-situ sensors 308, some of which are shown in FIG. 1 aswell as below. For example, FIG. 1 shows that planting machine 100-1 caninclude one or more fill level sensors 107 that detect a fill level ofmaterial in tanks 107. Fill level sensors can include float gauges,weight sensors that detect a weight of material in tanks 107, emittersensors that detect a level to which the material is filled, as well asvarious other types of sensors. Various components of agriculturalplanting machine 100-1 can be on individual parts of planting implement101, towing vehicle 10, or can be distributed in various ways acrossboth the planting implement 101 and towing vehicle 10. FIG. 1, alsoillustrates that towing vehicle can include, among other things,operator interface mechanisms 318 which can be used by an operator tomanipulate and control agricultural planting machine 100-1.

As shown, planting implement 101 is a row crop planter. In otherexamples, other types of planting machines can be used, such as airseeders. Planting implement 101 illustratively includes a toolbar 102that is part of a frame 104. FIG. 1 also shows that a plurality ofplanting row units 106 are mounted to the toolbar 102. Plantingimplement 101 can be towed behind towing vehicle 10, such as a tractor.FIG. 1 shows that material, such as seed, fertilizer, etc. can be storedin a tank 107 and pumped, using one or more pumps 115, through supplylines to the row units 106. The seed, fertilizer, etc., can also bestored on the row units 106 themselves. As shown in the illustratedexample of FIG. 1, each row unit can include a respective controller(s)135 which can be used to control operating parameters of each row unit106. In other examples, centralized controllers can control the rowunits 106.

FIG. 2 is a side view showing one example of a row unit 106. In theexample shown in FIG. 2, row unit 106 illustratively includes a chemicaltank 110 and a seed storage tank 112. It also illustratively includes afurrow opener 114 (e.g., opening disks) that opens a furrow in field107, a set of gauge wheels 116, and a furrow closer 118 (e.g., closingwheels) that close furrow 162. Seeds from tank 112 are fed by gravityinto a seed meter 124. The seed meter 124 controls the rate which seedsare dropped into a seed tube 120 or other seed delivery system, such asa brush belt or flighted brush belt (both shown below) from seed storagetank 112. The seeds can be sensed by a seed sensor 122. An actuator,such as motor, can be used to control the speed of seed meter 124 tocontrol the rate at which seeds are delivered to the furrow 162.

Some parts of the row unit 106 will now be discussed in more detail.First, it will be noted that there are different types of seed meters124, and the one that is shown is shown for the sake of example only andis described in greater detail below. For instance, in one example, eachrow unit 106 need not have its own seed meter. Instead, metering orother singulation or seed dividing techniques can be performed at acentral location, for groups of row units 106. The metering systems caninclude rotatable disks, rotatable concave or bowl-shaped devices, amongothers. The seed delivery system can be a gravity drop system (such asseed tube 120 shown in FIG. 2) in which seeds are dropped through theseed tube 120 and fall (via gravitational force) through the seed tubeand out the outlet end 121 into the furrow (or seed trench) 162. Othertypes of seed delivery systems are assistive systems, in that they donot simply rely on gravity to move the seed from the metering systeminto the ground. Instead, such systems actively capture the seeds fromthe seed meter and physically move the seeds from the meter to a loweropening where the exit into the ground or trench. Some examples of theseassistive systems are described in greater detail below.

FIG. 2 also shows an actuator 109 in a plurality of possible locations(109A, 109B, 109C, 109D, and 109E). Actuator 109 (e.g., pump) pumpsmaterial (such as fertilizer) from tank 107 through supply line 111 sothe material can be dispensed in or near the furrows. In such anexample, a controller can generate a control signal to control theactuation of pump 109. In other examples, actuators 109 are controllablevalves and one or more pumps 115 pump the material from tank(s) 107 toactuators 109 through supply line 111. In other examples, actuatorscontrol the delivery of material from other tanks, such as tank 110. Insuch an example, a controller controls the actuator by generating valveor actuator control signals. The control signal for each valve oractuator 109 can, in one example, be a pulse width modulated controlsignal. The flow rate through the corresponding actuator 109 can bebased on the duty cycle of the control signal (which controls the amountof time the valve is open and closed). It can be based on multiple dutycycles of multiple valves or based on other criteria. Further, thematerial can be applied in varying rates on a per-seed or per-plantbasis. For example, material may be applied at one rate when it is beingapplied at a location spaced from a seed location and at a second,higher, rate when it is being applied closer to the seed location. Inother examples, the material may be applied based on variouscharacteristics of the field, such as the nutrient levels, weedcharacteristics, as well as various other characteristics. These areexamples only.

Additionally, FIG. 2 shows a flow rate sensor 199 in a plurality ofpossible locations (199A, 199B, 199C, 199D, and 199E). Flow rate sensor199 can detect a volumetric flow rate of material flowing through supplyline 111.

Additionally, FIG. 2 shows that row unit 106 can include one or morefill level sensors, such as a fill level sensor 177 and a fill levelsensor 178. Fill level sensor 177 illustratively detects a fill level oftank 110. Fill level sensor 178 illustratively detects a fill level oftank 112. Fill level sensors 177 and 178 can include float gauges,weight sensors that detect a weight of material in tanks 110 and 112,emitter sensors that detect a level to which the material is filled, aswell as various other types of sensors.

In the example of shown in FIG. 2, material is passed, e.g., pumped orotherwise forced, through supply line 111 to an inlet end of actuator109. Actuator 109 is controlled by a controller (e.g., 135) to allow theliquid to pass from the inlet end of actuator 109 to an outlet end. Asmaterial passes through actuator 109, it travels through an applicationassembly 169 from a proximal end (which is attached to an outlet end ofactuator 109) to a distal tip (or application tip) 119, where the liquidis discharged into a trench, or proximate a trench or furrow 162 (e.g.,on the surface of field 107 next to trench or furrow 162 but not intrench or furrow 162), opened by disc opener 114.

A downforce generator or actuator 126 is mounted on a coupling assembly128 that couples row unit 106 to toolbar 102. Downforce actuator 126 canbe a hydraulic actuator, a pneumatic actuator, a spring-based mechanicalactuator or a wide variety of other actuators. In the example shown inFIG. 2, a rod 130 is coupled to a parallel linkage 132 and is used toexert an additional downforce (in the direction indicated by arrow 134)on row unit 106. The total downforce (which includes the force indicatedby arrow 134 exerted by actuator 126, plus the force due to gravityacting on the row unit 106, and indicated by arrow 136) is offset byupwardly directed forces acting on closing wheels 118 (from ground 138and indicated by arrow 140) and double disk opener 114 (again fromground 138 and indicated by arrow 142). The remaining force (the sum ofthe force vectors indicated by arrows 134 and 136, minus the forceindicated by arrows 140 and 142) and the force on any other groundengaging component on the row unit (not shown), is the differentialforce indicated by arrow 146. The differential force may also bereferred to herein as downforce margin. The force indicated by arrow 146acts on the gauge wheels 116. This load can be sensed by a gauge wheelload sensor 159 which may located anywhere on row unit 106 where it cansense that load. It can also be placed where may not sense the loaddirectly, but a characteristic indicative of that load. For example, itcan be disposed near a set of gauge wheel control arms (or gauge wheelarm) 148 that movably mount gauge wheels to shank 152 and control anoffset between gauge wheels 116 and the disks in double disk opener 114to control planting depth. Percent ground contact is a measure of apercentage of time that the load (downforce margin) on the gauge wheels116 is zero (indicating that the gauge wheels are out of contact withthe ground). The percent ground contact is calculated on the basis ofsensor data provided by the gauge wheel load sensor 159.

In addition, there may be other separate and controllable downforceactuators, such as one or more of a closing wheel downforce actuator 153that controls the downforce exerted on closing wheels 118. Closing wheeldownforce actuator 153 can be a hydraulic actuator, a pneumaticactuator, a spring-based mechanical actuator or a wide variety of otheractuators. The downforce exerted by closing wheel downforce actuator 153is represented by arrow 137. It will be understood that each row unit106 can include the various components described with reference to FIGS.2-6.

In the illustrated example, arms (or gauge wheel arms) 148illustratively abut a mechanical stop (or arm contact member or wedge)150. The position of mechanical stop 150 relative to shank 152 can beset by a planting depth actuator assembly 154. Control arms 148illustratively pivot around pivot point 156 so that, as planting depthactuator assembly 154 actuates to change the position of mechanical stop150, the relative position of gauge wheels 116, relative to the doubledisk opener 114, changes, to change the depth at which seeds areplanted.

In operation, row unit 106 travels generally in the direction indicatedby arrow 160. The double disk opener 114 opens the furrow 162 in thesoil 138, and the depth of the furrow 162 is set by planting depthactuator assembly 154, which, itself, controls the offset between thelowest parts of gauge wheels 116 and disk opener 114. Seeds are droppedthrough seed tube 120 into the furrow 162 and closing wheels 118 closethe soil.

As the seeds are dropped through seed tube 120, they can be sensed byseed sensor 122. Some examples of seed sensor 122 are an optical sensoror a reflective sensor, and can include a radiation transmitter and areceiver. The transmitter emits electromagnetic radiation and thereceiver the detects the radiation and generates a signal indicative ofthe presences or absences of a seed adjacent to the sensor. These arejust some examples of seed sensors. Row unit controller 335 may controlthe actuators 109 and/or pumps 115 based on the seed sensor signal tocontrollably apply material relative to the seed locations in the furrow162.

Also, as shown in FIG. 2, row unit 106 can include, as in-situ sensors308, one or more observation sensor systems 151. Observation sensorsystems 151 may include one or more sensors that detect one or morecharacteristics such as soil nutrient levels, weed characteristics, aswell as various other characteristics. In one example, an observationsensor system 151, such as the observation sensor system 151 disposedbetween opener 114 and closer 118 can detect characteristic of thefurrow as well as of the field proximate the furrow. Observation sensorsystems 151 may include one or more of an imaging system (e.g., stereoor mono camera), optical sensors, radar, lidar, ultrasonic sensors,infrared or thermal sensors, as well as a variety of other sensors. Insome examples, an observation sensor system 151 may detects seeds infurrow 162. Planting implement 101 can also include an observationsensor system 151 disposed to observe in front of opener 114, such asthe observation sensor system 151 shown mounted to toolbar 102. In otherexamples, observation sensor systems 151 can be mounted to various otherlocations of agricultural planting machine 100-1, such as various otherlocations on planting implement 101 or towing vehicle 10, or both.

FIG. 3 is similar to FIG. 2, and similar items are similarly numbered.However, instead of the seed delivery system being a seed tube 120 whichrelies on gravity to move the seed to the furrow 162, the seed deliverysystem shown in FIG. 4 is an assistive seed delivery system 166.Assistive seed delivery system 166 also illustratively has a seed sensor122 disposed therein. Assistive seed delivery system 166 captures theseeds as they leave seed meter 124 and moves them in a directionindicated by arrow 168 toward furrow 162. System 166 has an outlet end170 where the seeds exit system 166 into furrow 162 where they againreach their final seed position. System 166 may driven at variablespeeds by an actuator, such as a variable motor, which can be controlledby a controller (e.g., 135). The controller may control the speed ofsystem 166 based on various characteristics, such as nutrient levels,weed characteristics, etc. The controller may control the actuator 109to dispense material based on the seed sensor signal from seed sensor122 as well as the speed at which system 166 is driven. The controllermay control the actuator 109 based on various other characteristics,such as nutrient levels, weed characteristics, etc.

FIG. 3 also shows that row unit 106 can include a sensor 170 thatinteracts with the soil to detect various characteristics, such asnutrient levels of the soil. For instance, sensor 172 can be in the formof a probe that detects nutrient levels of the soil (such as the amount,or concentration, of various nutrients such as nitrogen, phosphorus,potassium, organic matter, etc.). In another example, sensor 172 can bein the form of an electromagnetic sensor that detects the capability ofthe soil to conduct or accumulate electrical charge, such as acapacitive sensor.

FIG. 4 shows one example of a rotatable mechanism that can be used aspart of the seed metering system (or seed meter) 124. The rotatablemechanism includes a rotatable disc, or concave element, 179. Concaveelement 179 has a cover (not shown) and is rotatably mounted relative tothe frame of row unit 106. Rotatable concave element 179 is driven by anactuator, such as a motor (not shown in FIG. 4), and has a plurality ofprojections or tabs 182 that are closely proximate correspondingapertures 184. A seed pool 186 is disposed generally in a lower portionsof an enclosure formed by rotating concave element 179 and itscorresponding cover. Rotatable concave element 179 is rotatably drivenby its motor (such as an electric motor, a pneumatic motor, a hydraulicmotor, etc.) for rotation generally in the direction indicated by arrow188, about a hub. A pressure differential is introduced into theinterior of the metering mechanism so that the pressure differentialinfluences seeds from seed pool 186 to be drawn to apertures 184. Forinstance, a vacuum can be applied to draw the seeds from seed pool 186so that they come to rest in apertures 184, where the vacuum holds themin place. Alternatively, a positive pressure can be introduced into theinterior of the metering mechanism to create a pressure differentialacross apertures 184 to perform the same function.

Once a seed comes to rest in (or proximate) an aperture 184, the vacuumor positive pressure differential acts to hold the seed within theaperture 184 such that the seed is carried upwardly generally in thedirection indicated by arrow 188, from seed pool 186, to a seeddischarge area 190. It may happen that multiple seeds are residing in anindividual seed cell. In that case, a set of brushes or other members194 that are located closely adjacent the rotating seed cells tend toremove the multiple seeds so that only a single seed is carried by eachindividual cell. Additionally, a seed sensor 193 can also illustrativelybe mounted adjacent to rotating element 181. Seed sensor 193 detects andgenerates a signal indicative of seed presence.

Once the seeds reach the seed discharge area 190, the vacuum or otherpressure differential is illustratively removed, and a positive seedremoval wheel or knock-out wheel 191, can act to remove the seed fromthe seed cell. Wheel 191 illustratively has a set of projections 195that protrude at least partially into apertures 184 to actively dislodgethe seed from those apertures. When the seed is dislodged (such as seed171), it is illustratively moved by the seed tube 120, seed deliverysystem 166 (some examples of which are shown above and below) to thefurrow 162 in the ground.

FIG. 5 shows an example where the rotating element 181 is positioned sothat its seed discharge area 190 is above, and closely proximate,assistive seed delivery system 166. In the example shown in FIG. 5,assistive seed delivery system 166 includes a transport mechanism suchas a belt 200 with a brush that is formed of distally extending bristles202 attached to belt 200 that act as a receiver for the seeds. Belt 200is mounted about pulleys 204 and 206. One of pulleys 204 and 206 isillustratively a drive pulley while the other is illustratively an idlerpulley. The drive pulley is illustratively rotatably driven by anactuator, such a conveyance motor (not shown in FIG. 5), which can be anelectric motor, a pneumatic motor, a hydraulic motor, etc. Belt 200 isdriven generally in the direction indicated by arrow 208

Therefore, when seeds are moved by rotating element 181 to the seeddischarge area 190, where they are discharged from the seed cells inrotating element 181, they are illustratively positioned within thebristles 202 by the projections 182 that push the seed into thebristles. Assistive seed delivery system 166 illustratively includeswalls that form an enclosure around the bristles, so that, as thebristles move in the direction indicated by arrow 208, the seeds arecarried along with them from the seed discharge area 190 of the meteringmechanism, to a discharge area 210 either at ground level, or belowground level within a trench or furrow 162 that is generated by thefurrow opener 114 on the row unit 106.

Additionally, a seed sensor 203 is also illustratively coupled toassistive seed delivery system 166. As the seeds are moved in bristles202 past sensor 203, sensor 203 can detect the presence or absence of aseed. Some examples of seed sensor 203 includes an optical sensor orreflective sensor.

FIG. 6 is similar to FIG. 5, except that seed delivery system 166 is notformed by a belt with distally extending bristles. Instead, it is formedby a flighted belt (transport mechanism) in which a set of paddles 214form individual chambers (or receivers), into which the seeds aredropped, from the seed discharge area 190 of the metering mechanism. Theflighted belt moves the seeds from the seed discharge area 190 to theexit end 210 of the flighted belt, within the trench or furrow 162.

There are a wide variety of other types of seed delivery systems aswell, that include a transport mechanism and a receiver that receives aseed. For instance, they include dual belt delivery systems in whichopposing belts receive, hold and move seeds to the furrow, a rotatablewheel that has sprockets which catch seeds from the metering system andmove them to the furrow, multiple transport wheels that operate totransport the seed to the furrow, an auger, among others.

FIG. 7 shows one example of a mobile agricultural material applicationmachine 100 as a mobile agricultural sprayer 100-2 that includes atowing vehicle 240 and a towed spraying implement 224. Though, in otherexamples, such as the example shown in FIG. 8, sprayer 100-2 can beself-propelled. Additionally, other types of material applicationmachines are contemplated, such as dry material spreaders. FIG. 7 alsoshows that mobile agricultural sprayer 100-2 can include one or morein-situ sensors 308, some of which are shown in FIG. 7 as well as below.For example, FIG. 7 shows that sprayer 100-2 can include one or morefill level sensors 271 that detect a fill level of material in tanks234. Fill level sensors can include float gauges, weight sensors thatdetect a weight of material in tanks 234, emitter sensors that detect alevel to which the material is filled, as well as various other types ofsensors.

Sprayer 100-2 includes a spraying system having one or more tanks 234containing one or more materials, such as a liquid materials (e.g.,fertilizer, herbicide, pesticide, etc.), that is to be applied to field207. Tanks 234 are fluidically coupled to spray nozzles 230 by adelivery system comprising a set of conduits. One or more pumps areconfigured to pump the product from the tanks 234 through the conduitsand through nozzles 230 to apply the product to the field 207. In someexamples, the fluid pumps are actuated by operation of one or moremotors, such as electric motors, pneumatic motors, or hydraulic motors,that drive the pumps.

Spray nozzles 230 are coupled to, and spaced apart along, boom 220. Boom220 includes arms 221 and 222 which can articulate or pivot relative toa center frame 226. Thus, arms 221 and 222 are movable between a storageor transport position and an extended or deployed position (shown inFIG. 7). The boom 220, including each arm 221 and 222, can includemultiple discrete and controllable sections which are supplied productfrom tanks 234 by the fluid pumps through a respective conduit of eachsection.

Each section can include a respective set of one or more spray nozzles230. Each section can be activated or deactivated through the actuationof a corresponding controllable actuator, such as a valve, for instance,a section can be deactivated, that is the section or the nozzles of thesection, or both, are prevented from receiving fluid, by actuation of acontrollable actuator that is upstream of the section or the nozzles, orboth. In some examples, the nozzles of the section may each have anassociated controllable actuator which can be actuated to activate ordeactivate the nozzles. The application rate of product is the rate(volumetric rate) at which product is applied to the field over whichsprayer 100-2 travels. The application rate corresponds to a volumetricflow rate of the product from the tanks 234 through the spray nozzles230. The volumetric flow rate is controlled by operation of actuators(such as fluid pumps or valves), such as by varying the speed ofactuation of the pump with an associated motor or by controllablyopening and closing a vale. In some examples, a controllable valve thatcorresponds to each section or to each nozzle, can be operable toreciprocate (e.g., pulse) between a closed state and an open state atvariable frequency (e.g., pulse width modulation control) to control therate at which the product is discharged from the set of spray nozzles230 of the respective section or from the respective individual spraynozzle 230.

In the example illustrated in FIG. 7, agricultural sprayer 100-2comprises a towed implement 224 that carries the spraying system, and atowing or support machine 240 (illustratively a tractor, which may besimilar to towing vehicle 10) that tows the towed spraying implement224. Towed implement 224 includes a set of ground engaging elements 243,such as wheels or tracks. Towing machine 240 includes a set of groundengaging elements 244, such as wheels or tracks. In the exampleillustrated, towing machine 240 includes an operator compartment or cab228, which can include a variety of different operator interfacemechanisms (e.g., 318 shown in FIG. 10) for controlling sprayer 110-2.

As will be shown in FIG. 8, an agricultural sprayer can beself-propelled. That is, rather than being towed by a towing machine,the machine that carries the spraying system also includes propulsionand steering systems.

FIG. 7 also illustrates that agricultural sprayer 100-2 can include oneor more observation sensor systems 251. Observation sensors systems 251can be located at various locations on sprayer 100-2, such as on towingvehicle 240 or implement 224, or both. As illustrated, sprayer 100-2includes an observation sensor system 251 on towing vehicle 240 as wellas a plurality of observation sensor systems disposed on each of arm 221and arm 222 of boom 220. Observation sensor systems 251 can detect avariety of characteristic at the field, for example, soil nutrients,weed characteristics, as well as a variety of other characteristics.Observation sensor systems 251 may include one or more of an imagingsystem (e.g., stereo or mono camera), optical sensors, radar, lidar,ultrasonic sensors, infrared or thermal sensors, as well as a variety ofother sensors.

FIG. 8 illustrates one example of an agricultural sprayer 100-3 that isself-propelled as an example mobile material application machine 100. InFIG. 8, sprayer 100-3 has an on-board spraying system, including, amongother things, one or more tanks 255 containing one or more materials(e.g., fertilizer, herbicide, pesticide, etc.) a boom 254, that iscarried on a machine frame 256 having an operator compartment 259, and aset of ground engaging elements 260, such as wheels or tracks. Operatorcompartment 259 can include a variety of different operator interfacemechanisms (e.g., 318 shown in FIG. 10) for controlling agriculturalsprayer 100-3. Tank(s) 255 are fluidically coupled to spray nozzles 258by a delivery system comprising a set of conduits. One or more fluidpumps are configured to pump the material from tank(s) 255 through theconduits and through nozzles 258 to apply the material to the field overwhich agricultural sprayer 100-3 travels. In some examples, the fluidpumps are actuated by operation of one or more motors, such as electricmotors, pneumatic motors, or hydraulic motors, that drive the pumps.

Spray nozzles 258 are coupled to, and spaced apart along, boom 254. Boom254 includes arms 262 and 267 which can articulate or pivot relative toa center frame 266. Thus, arms 262 and 264 are movable between a storageor transport position and an extended or deployed position (shown inFIG. 8). The boom 254, including each arm 262 and 267, can includemultiple discrete and controllable sections which are supplied productfrom tank(s) 255 by the fluid pump(s) through a respective conduit ofeach section.

Each section can include a respective set of one or more spray nozzles258. Each section can be activated or deactivated through the actuationof a corresponding controllable actuator, such as a valve, for instance,a section can be deactivated, that is the section or the nozzles of thesection, or both, are prevented from receiving fluid, by actuation of acontrollable actuator that is upstream of the section or the nozzles, orboth. In some examples, the nozzles of the section may each have anassociated controllable actuator which can be actuated to activate ordeactivate the nozzles. The application rate of product is the rate(volumetric rate) at which product is applied to the field over whichsprayer 100-3 travels. The application rate corresponds to a volumetricflow rate of the product from the tanks 255 through the spray nozzles258. The volumetric flow rate is controlled by operation of actuators(such as fluid pumps or valves), such as by varying the speed ofactuation of the pump with an associated motor or by controllablyopening and closing a valve. In some examples, a controllable valve thatcorresponds to each section or to each nozzle, can be operable toreciprocate (e.g., pulse) between a closed state and an open state atvariable frequency (e.g., pulse width modulation control) to control therate at which the product is discharged from the set of spray nozzles258 of the respective section or from the respective individual spraynozzle 258.

FIG. 8 also shows that agricultural sprayer 100-3 can include one ormore in-situ sensors 308, some of which are shown in FIG. 8 as well asbelow. For example, FIG. 8 shows that sprayer 100-3 can include one ormore fill level sensors 271 that detect a fill level of material intanks 255. Fill level sensors can include float gauges, weight sensorsthat detect a weight of material in tanks 255, emitter sensors thatdetect a level to which the material is filled, as well as various othertypes of sensors.

Additionally, FIG. 8 shows that sprayer 100-3 can include one or moreobservation sensor systems 251. Observation sensors systems 251 can belocated at various locations on sprayer 100-3. As illustrated, sprayer100-3 includes an observation sensor system 251 coupled to the roof (orframe) of operator compartment 259 as well as a plurality of observationsensor systems 251 disposed on each of arm 262 and arm 267 of boom 254.Observation sensor systems 251 can detect a variety of characteristic atthe field, for example, soil nutrients, weed characteristics, as well asa variety of other characteristics. Observation sensor systems 251 mayinclude one or more of an imaging system (e.g., stereo or mono camera),optical sensors, radar, lidar, ultrasonic sensors, infrared or thermalsensors, as well as a variety of other sensors.

It will be noted that while various examples of in-situ sensors 308 areshown in FIGS. 1-8, a mobile agricultural material application machine100 (e.g., 100-1, 100-2, 100-3) can include various other types ofsensors, some of which will be discussed in FIG. 10.

FIG. 9 shows one example of a material delivery machine 379. FIG. 9shows that a material delivery machine 379 can include a towing vehicleand towed implement, such as a truck (e.g., semi-truck) 280 and trailer(e.g., semi-trailer) 282. Various other forms of material deliverymachines are contemplated herein. For instance, in other examples, thematerial delivery machine 379 may not include a towed implement,instead, the material container may be integrated on the frame (e.g.,chassis) of the vehicle.

Truck 280, as illustrated, includes a power plant 283 (e.g., internalcombustion engine, battery and electric motors, etc.), ground engagingelements 285 (e.g., wheels or tracks), and an operator compartment 287.The operator compartment can include a variety of operator interfacemechanisms, which can be similar to operator interface mechanisms 318shown in FIG. 10. In some examples, truck 280 may be autonomous orsemi-autonomous. Trailer 282 is coupled to track by way of a connectionassembly (e.g., one or more of a hitch, electrical coupling, hydrauliccoupling, pneumatic coupling, etc.) and, as illustrated, includes groundengaging elements 290, such as wheels or tracks, and a materialcontainer 292 which includes a volume to store or hold one or morematerials (dry or liquid), such as seed, fertilizer, herbicide,pesticide, etc.

In some examples, material delivery machine 379 can also include amaterial transfer subsystem (not shown) which can include a conduit(e.g., a chute, a hose, a line, a pipe, etc.) through which material canbe conveyed by an actuator such as an auger, a pump, a motor, etc. Inother examples, the material transfer subsystem may comprise anactuatable door disposed on the bottom side of the material container292 that is actuatable between an open position and a closed position.These are merely some examples.

FIG. 10 is a block diagram showing some portions of an agriculturalmaterial application system architecture 300. FIG. 3 shows thatagricultural material application system architecture 300 includesmobile agricultural material application machine 100 (e.g., 100-1,100-2, 100-3, etc.). Agricultural material application system 300 alsoincludes one or more remote computing systems 368, one or more remoteuser interfaces 364, network 359, delivery vehicle(s) 379, deliveryservice system(s) 377, and one or more information maps 358. Mobileagricultural material application machine 100, itself, illustrativelyincludes one or more processors or servers 301, data store 302,communication system 306, one or more in-situ sensors 308 that sense oneor more characteristics at a field concurrent with an operation, and aprocessing system 338 that processes the sensor data (e.g., sensorsignals, images, etc.) generated by in-situ sensors 308 to generateprocessed sensor data. The in-situ sensors 308 generate valuescorresponding to the sensed characteristics. Mobile machine 100 alsoincludes a predictive model or relationship generator (collectivelyreferred to hereinafter as “predictive model generator 310”), predictivemodel or relationship (collectively referred to hereinafter as“predictive model 311”), predictive map generator 312, control zonegenerator 313, control system 314, one or more controllable subsystems316, and an operator interface mechanism 318. The mobile machine canalso include a wide variety of other machine functionality 320.

The in-situ sensors 308 can be on-board mobile machine 100, remote frommobile machine 100, such as deployed at fixed locations on the worksiteor on another machine operating in concert with mobile machine 100, suchas an aerial vehicle, and other types of sensors, or a combinationthereof. In-situ sensors 308 sense characteristics at the worksiteduring the course of an operation. In-situ sensors 308 illustrativelyinclude one or more weed sensors 372, one or more nutrient sensors 374,one or more material consumption sensors 376, geographic positionsensors 304, heading/speed sensors 325, and can include various othersensors 328, such as the various other sensors described above.

Weed sensors 372 illustratively detect values of weed characteristicswhich can be indicative of weed presence, weed location, weed type, weedintensity, as well as various other weed characteristics. Weed sensors372 can be located at various locations on material application machine100 and can be configured to detect weed characteristics at the fieldahead of material application machine 100 or ahead of a given componentof material application machine 100, or both. Weed sensors 372 mayinclude one or more of an imaging system (e.g., stereo or mono camera),optical sensors, radar, lidar, ultrasonic sensors, infrared or thermalsensors, as well as a variety of other sensors. In some examples, weedsensors 372 can be similar to observation sensors systems 151 or 251.These are merely some examples. Weed sensors 372 can be any of a varietyof different types of sensors.

Nutrient sensors 374 illustratively detect nutrient values. Nutrientvalues can be indicative of an amount (e.g., concentration) of one ormore nutrients in the soil of the field, such as the amount of nitrogen,the amount of potassium, the amount of phosphate, the amount of organicmatter, the amount of one or more micronutrients, etc. Thus, in someexamples, nutrient values are or include soil nutrient values.Alternatively, or additionally, nutrient values can be indicative of anamount (e.g., concentration) of one or more nutrients in the plants atthe field. Nutrients of the plant can include various types ofnutrients, such as boron, sulphur, manganese, zinc, magnesium,phosphorus, calcium, iron, copper, molybdenum, potassium, nitrogen,etc., as well as constituents of the crop such as protein, sugar,starch, ligan, etc. Thus, in some examples, nutrient values are orinclude plant nutrient values. As can be seen, nutrient values may besoil nutrient values or plant nutrient values, or both. In someexamples, the nutrient values may be binary in that they indicatesufficient or deficient levels (e.g., relative to a threshold) ratherthan a valued amount. Nutrient sensors 374 can be located at variouslocations on material application machine 100 and can be configured todetect nutrient characteristics (e.g., soil nutrient characteristics orplant nutrient characteristics, or both) at the field ahead of materialapplication machine 100 or ahead of a given component of materialapplication machine 100, or both. In some examples, nutrient sensors 374detect the soil or a characteristic of the soil to detect nutrientvalues. For instance, nutrient sensors 374 may detect the color of thesoil, the thermal characteristics of the soil, the emissivity orabsorption of electromagnetic radiation, the capability of the soil toconduct or accumulate electrical charge, as well as various othercharacteristics. In some examples, nutrient sensors 374 detect thevegetation (e.g., plants) or a characteristic of the vegetation (e.g.,plants) at the field to detect nutrient values. For instance, nutrientsensors 374 may detect the plant size, the plant health, the plantcoloration, constituents of the plant (e.g., protein, starch, sugar,lignan, etc.), the emissivity or absorption of electromagneticradiation, characteristics of the components of the plant (e.g.,characteristics of the leaves, characteristics of the leaf buds,characteristics of the stalk, etc.), as well as various othercharacteristics. Nutrient sensors 374 may include one or more of animaging system (e.g., stereo or mono camera), optical sensors, radar,lidar, ultrasonic sensors, infrared or thermal sensors, soil probes,electromagnetic sensors that detect the capability of the soil toconduct or accumulate electrical charge, as well as a variety of othersensors. In some examples, nutrient sensors 374 can be similar toobservation sensors systems 151 or 251. In some examples, nutrientsensors 374 can be similar to sensor 170. These are merely someexamples. Nutrient sensors 374 can be any of a variety of differenttypes of sensors.

Material consumption sensors 376 illustratively detect materialconsumption values which can be indicative of the amount of material(e.g., seed, dry or liquid material, fertilizer, herbicide, pesticide,etc.) consumed (e.g., used) by material application machine 100 at thefield. Material consumption sensors 374 can be located at variouslocations on material application machine. Material consumption sensors374 can include fill level sensors (e.g., 117, 177, 178, 271, etc.) thatdetect a fill level of a material container, such as tanks 107, tanks110, tanks 112, tanks 234, and tanks 255. In some examples, materialconsumption sensors 374 can detect a flow rate of material, such as flowsensors (e.g., flow meters) that detect a volumetric flow of materialthrough a delivery line (e.g., 111, or conduits of boom 220, or conduitsof boom 254, etc.). In some examples, material consumption sensors 374can provide a count of the material consumed, for example, seed sensors,such as seed sensors 122, 193, or 203. In some examples, observationsensor systems 151 may detect the material consumed, such as anobservation sensor system disposed to observe the trench or furrow 162.In some examples, material consumption sensors 376 can include sensorsthat detect the operating parameters of one or more actuators, such asthe speed (or rate) at which the actuators actuate to control the rateof material. These are merely some examples. Material consumptionsensors 376 can be any of a variety of different types of sensors.

Geographic position sensors 304 illustratively sense or detect thegeographic position or location of mobile machine 100. Geographicposition sensors 304 can include, but are not limited to, a globalnavigation satellite system (GNSS) receiver that receives signals from aGNSS satellite transmitter. Geographic position sensors 304 can alsoinclude a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensors 304 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

Heading/speed sensors 325 detect a heading and speed at which mobilemachine 100 is traversing the worksite during the operation. This caninclude sensors that sense the movement of ground-engaging elements(e.g., wheels or tracks), or can utilize signals received from othersources, such as geographic position sensor 304, thus, whileheading/speed sensors 325 as described herein are shown as separate fromgeographic position sensor 304, in some examples, machine heading/speedis derived from signals received from geographic positions sensor 304and subsequent processing. In other examples, heading/speed sensors 325are separate sensors and do not utilize signals received from othersources.

Other in-situ sensors 328 may be any of a wide variety of other sensors,including the other sensors described above with respect to FIGS. 1-8.Other in-situ sensors 328 can be on-board mobile machine 100 or can beremote from mobile machine 100, such as other in-situ sensors 328on-board another mobile machine that capture in-situ data ofcharacteristics at the field or sensors at fixed locations throughoutthe field. The remote data from remote sensors can be obtained by mobilemachine 100 via communication system 306 over network 359.

In-situ data includes data taken from a sensor on-board the mobilemachine 100 or taken by any sensor where the data are detected duringthe operation of mobile machine 100 at a field.

Processing system 338 processes the sensor data (e.g., signals, images,etc.) generated by in-situ sensors 308 to generate processed sensor dataindicative of one or more characteristics. For example, processingsystem generates processed sensor data indicative of characteristicvalues based on the sensor data generated by in-situ sensors 308, suchas weed values based on sensor data generated by weed sensors 372,nutrient values based on sensor data generated by nutrient sensors 374,and material consumption values based on sensor data generated bymaterial consumption sensors 376. Processing system 338 also processessensor data generated by other in-situ sensors 308 to generate processedsensor data indicative of other characteristic values, such as machinespeed characteristic (travel speed, acceleration, deceleration, etc.)values based on sensor data generated by heading/speed sensors 325,machine heading values based on sensor data generated by heading/speedsensors 325, geographic position (or location) values based on sensordata generated by geographic position sensors 304, as well as variousother values based on sensors signals generated by various other in-situsensors 328.

It will be understood that processing system 338 can be implemented byone or more processers or servers, such as processors or servers 301.Additionally, processing system 338 can utilize various sensor signalfiltering functionalities, noise filtering functionalities, sensorsignal categorization, aggregation, normalization, as well as variousother processing functionalities. Similarly, processing system 338 canutilize various image processing functionalities such as, sequentialimage comparison, RGB, edge detection, black/white analysis, machinelearning, neural networks, pixel testing, pixel clustering, shapedetection, as well any number of other suitable processing and dataextraction functionalities.

FIG. 10 also shows that an operator 360 may operate mobile machine 100.The operator 360 interacts with operator interface mechanisms 318. Insome examples, operator interface mechanisms 318 may include joysticks,levers, a steering wheel, linkages, pedals, buttons, dials, keypads,user actuatable elements (such as icons, buttons, etc.) on a userinterface display device, a microphone and speaker (where speechrecognition and speech synthesis are provided), among a wide variety ofother types of control devices. Where a touch sensitive display systemis provided, operator 360 may interact with operator interfacemechanisms 318 using touch gestures. In some examples, at least someoperator interface mechanisms 318 may be disposed in an operatorcompartment of mobile machine 100. In some examples, at least someoperator interface mechanisms 318 may be remote (or separable) frommobile machine 100 but are in communication therewith. Thus, theoperator 360 may be local or remote. These examples described above areprovided as illustrative examples and are not intended to limit thescope of the present disclosure. Consequently, other types of operatorinterface mechanisms 318 may be used and are within the scope of thepresent disclosure.

FIG. 10 also shows remote users 366 interacting with mobile machine 100or remote computing systems 368, or both, through user interfacesmechanisms 364 over network 359. In some examples, user interfacemechanisms 364 may include joysticks, levers, a steering wheel,linkages, pedals, buttons, dials, keypads, user actuatable elements(such as icons, buttons, etc.) on a user interface display device, amicrophone and speaker (where speech recognition and speech synthesisare provided), among a wide variety of other types of control devices.Where a touch sensitive display system is provided, user 366 mayinteract with user interface mechanisms 364 using touch gestures. Theseexamples described above are provided as illustrative examples and arenot intended to limit the scope of the present disclosure. Consequently,other types of user interface mechanisms 364 may be used and are withinthe scope of the present disclosure.

Remote computing systems 368 can be a wide variety of different types ofsystems, or combinations thereof. For example, remote computing systems368 can be in a remote server environment. Further, remote computingsystems 368 can be remote computing systems, such as mobile devices, aremote network, a farm manager system, a vendor system, or a widevariety of other remote systems. In one example, mobile machine 100 canbe controlled remotely by remote computing systems 368 or by remoteusers 366, or both. As will be described below, in some examples, one ormore of the components shown being disposed on mobile machine 100 inFIG. 10 can be located elsewhere, such as at remote computing systems368.

FIG. 10 also shows that one or more delivery vehicles 379 can interactwith other items in agricultural material application system 300 overnetwork 359. For instance, communication system 306 of mobileagricultural material application machine 100 may communicate with oneor more delivery vehicles 379 to provide information such as materialdelivery locations and material delivery times to schedule materialdelivery. In another example, material one or more delivery vehicles 379may be controlled, such as by control system 314, to travel to amaterial delivery location.

FIG. 10 also shows that one or more delivery service systems 380 caninteract with other items in agricultural material application system300 over network 359. For instance, communication system 306 of mobileagricultural material application machine 100 may communicate withmaterial delivery service systems 380 to provide information such asmaterial delivery locations and material delivery times to schedulematerial delivery. Material delivery service systems 380 can be a widevariety of different types of systems, or combinations thereof. Forexample, material delivery service systems 380 can be in a remote serverenvironment. Further, material delivery service systems 380 can beremote computing systems, such as mobile devices, a remote network, avendor system, or a wide variety of other remote systems.

FIG. 10 also shows that mobile machine 100 can obtain one or moreinformation maps 358. As described herein, the information maps 358include, for example, a soil property map, a yield map, a residue map, aconstituents map, a seeding map, a topographic map, a vegetative index(VI) map, an optical map, a weed map, a contamination map, as well asvarious other maps. However, information maps 358 may also encompassother types of data, such as other types of data that were obtainedprior to a current operation or a map from a prior operation. In otherexamples, information maps 358 can be generated during a currentoperation, such a map generated by predictive map generator 312 based ona predictive model 311 generated by predictive model generator 310.

Information maps 358 may be downloaded onto mobile material applicationmachine 100 over network 359 and stored in data store 302, usingcommunication system 306 or in other ways. In some examples,communication system 306 may be a cellular communication system, asystem for communicating over a wide area network or a local areanetwork, a system for communicating over a near field communicationnetwork, or a communication system configured to communicate over any ofa variety of other networks or combinations of networks. Network 359illustratively represents any or a combination of any of the variety ofnetworks. Communication system 306 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card or both.

As described above, the present description relates to the use of modelsto predict one or more characteristics at the field at which mobilematerial application machine 100 is operating. The models 311 can begenerated by predictive model generator 310, during the currentoperation.

In one example, predictive model generator 310 generates a predictivemodel 311 that is indicative of a relationship between the values sensedby the in-situ sensors 308 and values mapped to the field by theinformation maps 358. For example, if the information map 358 mapsvalues of a characteristic to different locations in the worksite, andthe in-situ sensor 308 are sensing values indicative of a characteristic(e.g., weed values, nutrient values, material consumption values, orspeed characteristic values), then model generator 310 generates apredictive model that models the relationship between the values of themapped characteristic and the values of the sensed characteristic.

In some examples, the predictive map generator 312 uses the predictivemodels generated by predictive model generator 310 to generatefunctional predictive maps that predict the value of a characteristic,sensed by the in-situ sensors 308, at different locations in the fieldbased upon one or more of the information maps 358.

For example, where the predictive model 311 is a predictive nutrientmodel that models a relationship between nutrient values sensed byin-situ sensors 308 and one or more of soil property values from a soilproperty map, yield values from a yield map, residue values from aresidue map, constituent values from a constituents map, seedingcharacteristic values from a seeding map, topographic characteristicvalues from a topographic map, vegetative index values from a vegetativeindex map, and other characteristic values from another information map358, then predictive map generator 312 generates a functional predictivenutrient map that predicts nutrient values at different locations at theworksite based on one or more of the mapped values at those locationsand the predictive nutrient model.

In another example, where the predictive model 311 is a predictive weedmodel that models a relationship between weed values sensed by in-situsensors 308 and one or more of vegetative index values from a vegetativeindex map, optical characteristic values from an optical map, weedvalues from a weed map, and other characteristic values from anotherinformation map 358, then predictive map generator 312 generates afunctional predictive weed map that predicts weed values at differentlocations at the worksite based on one or more of the mapped values atthose locations and the predictive weed model.

In another example, where the predictive model 311 is a predictivematerial consumption model that models a relationship between materialconsumption values sensed by in-situ sensors 308 and one or more of soilproperty values from a soil property map, weed values from a weed map,contamination values from a contamination map, vegetative index valuesfrom a vegetative index map, topographic characteristic values from atopographic map, and other characteristic values from anotherinformation map 358, then predictive map generator 312 generates afunctional predictive material consumption map that predicts materialconsumption values at different locations at the worksite based on oneor more of the mapped values at those locations and the predictivematerial consumption model.

In another example, where the predictive model 311 is a predictive speedmodel that models a relationship between speed characteristic valuessensed by in-situ sensors 308 and values of one or more characteristicsfrom one or more information maps 358, then predictive map generator 312generates a functional predictive speed map that maps predictive speedcharacteristic values at different locations at the worksite based on ormore of the mapped values at those locations and the predictive speedmodel.

In some examples, the type of values in the functional predictive map263 may be the same as the in-situ data type sensed by the in-situsensors 308. In some instances, the type of values in the functionalpredictive map 263 may have different units from the data sensed by thein-situ sensors 308. In some examples, the type of values in thefunctional predictive map 263 may be different from the data type sensedby the in-situ sensors 308 but have a relationship to the type of datatype sensed by the in-situ sensors 308. For example, in some examples,the data type sensed by the in-situ sensors 308 may be indicative of thetype of values in the functional predictive map 363. In some examples,the type of data in the functional predictive map 363 may be differentthan the data type in the information maps 358. In some instances, thetype of data in the functional predictive map 263 may have differentunits from the data in the information maps 358. In some examples, thetype of data in the functional predictive map 263 may be different fromthe data type in the information map 358 but has a relationship to thedata type in the information map 358. For example, in some examples, thedata type in the information maps 358 may be indicative of the type ofdata in the functional predictive map 263. In some examples, the type ofdata in the functional predictive map 263 is different than one of, orboth of, the in-situ data type sensed by the in-situ sensors 308 and thedata type in the information maps 358. In some examples, the type ofdata in the functional predictive map 263 is the same as one of, or bothof, of the in-situ data type sensed by the in-situ sensors 308 and thedata type in information maps 358. In some examples, the type of data inthe functional predictive map 263 is the same as one of the in-situ datatype sensed by the in-situ sensors 308 or the data type in theinformation maps 358, and different than the other.

As shown in FIG. 10, predictive map 264 predicts the value of a sensedcharacteristic (sensed by in-situ sensors 308), or a characteristicrelated to the sensed characteristic, at various locations across theworksite based upon one or more information values in one or moreinformation maps 358 at those locations and using a predictive model311. For example, if predictive model generator 310 has generated apredictive model indicative of a relationship between soil propertyvalues and values of a characteristic sensed by in-situ sensors 308then, given the soil property value at different locations across theworksite, predictive map generator 312 generates a predictive map 264that predicts values of the sensed characteristic at different locationsacross the worksite. The soil property value, obtained from the soilproperty map, at those locations and the relationship between soilproperty values and the values of the sensed characteristic, obtainedfrom a predictive model 311, are used to generate the predictive map264. This is merely one example.

Some variations in the data types that are mapped in the informationmaps 358, the data types sensed by in-situ sensors 308, and the datatypes predicted on the predictive map 264 will now be described.

In some examples, the data type in one or more information maps 358 isdifferent from the data type sensed by in-situ sensors 308, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 308. For instance, the information map 358 may be aseeding map, and the variable sensed by the in-situ sensors 308 may be anutrient value. The predictive map 264 may then be a predictive nutrientmap that maps predictive nutrient values to different geographiclocations in the in the worksite. In another example, the informationmap 358 may be a vegetative index map, and the variable sensed by thein-situ sensors 308 may be a weed value. The predictive map 264 may thenbe a predictive weed map that maps predictive weed values to differentgeographic locations in the in the worksite. In another example, theinformation map 358 may be a contamination map, and the variable sensedby the in-situ sensors 308 may be a material consumption value. Thepredictive map 264 may then be a predictive material consumption mapthat maps predictive material consumption values to different geographiclocations in the in the worksite. In another example, the informationmap 358 may be a soil property map, and the variable sensed by thein-situ sensors 308 may be a speed characteristic value. The predictivemap 264 may then be a predictive speed map that maps predictive speedcharacteristic values to different geographic locations in the in theworksite.

Also, in some examples, the data type in the information map 358 isdifferent from the data type sensed by in-situ sensors 308, and the datatype in the predictive map 264 is different from both the data type inthe information map 358 and the data type sensed by the in-situ sensors308. For example, the information map may be a residue map, and thevariable sensed by the in-situ sensors 308 may be a color of the plants.The predictive map may then be a predictive nutrient map that mapspredictive nutrient values to the different geographic locations in theworksite.

In some examples, the information map 358 is from a prior pass throughthe field during a prior operation and the data type is different fromthe data type sensed by in-situ sensors 308, yet the data type in thepredictive map 264 is the same as the data type sensed by the in-situsensors 308. For instance, the information map 358 may be a seeding mapgenerated during a previous planting operation on the field, and thevariable sensed by the in-situ sensors 308 may be a nutrient value. Thepredictive map 264 may then be a predictive nutrient map that mapspredictive nutrient values to different geographic locations in thefield. In another example, the information map 358 may be a vegetativeindex map generated during a previous operation on the field, and thevariable sensed by the in-situ sensors 308 may be a weed value. Thepredictive map 264 may then be a predictive weed map that mapspredictive weed values to different geographic locations in the field.In another example, the information map 358 may be a topographic mapgenerated during a previous operation on the field, and the variablesensed by the in-situ sensors 308 may be a material consumption value.The predictive map 264 may then be a predictive material consumption mapthat maps predictive material consumption values to different geographiclocations in the field. In another example, the information map 358 maybe a soil property map generated during a previous operation on thefield, and the variable sensed by the in-situ sensors 308 may be a speedcharacteristic value. The predictive map 264 may then be a predictivespeed map that maps predictive speed characteristic values to differentgeographic locations in the field.

In some examples, the information map 358 is from a prior pass throughthe field during a prior operation and the data type is the same as thedata type sensed by in-situ sensors 308, and the data type in thepredictive map 264 is also the same as the data type sensed by thein-situ sensors 308. For instance, the information map 358 may be a weedmap generated during a previous year or earlier in the same season, andthe variable sensed by the in-situ sensors 308 may be a weed value. Thepredictive map 264 may then be a predictive weed map that mapspredictive weed values to different geographic locations in the field.In such an example, the relative weed value differences in thegeoreferenced information map 358 from the prior year or earlier in thesame season can be used by predictive model generator 310 to generate apredictive model that models a relationship between the relative weedvalue differences on the information map 358 and the weed values sensedby in-situ sensors 308 during the current operation. The predictivemodel is then used by predictive map generator 310 to generate apredictive weed map. In another example, the information map 358 may bea nutrient map generated during a previous year or earlier in the sameseason, and the variable sensed by the in-situ sensors 308 may be anutrient value. The predictive map 264 may then be a predictive nutrientmap that maps predictive nutrient values to different geographiclocations in the field. In such an example, the relative nutrient valuedifferences in the georeferenced information map 358 from the prior yearor earlier in the same season can be used by predictive model generator310 to generate a predictive model that models a relationship betweenthe relative nutrient value differences on the information map 358 andthe nutrient values sensed by in-situ sensors 308 during the currentoperation. The predictive model is then used by predictive map generator310 to generate a predictive nutrient map. In another example, theinformation map 358 may be a material consumption map generated during aprevious year or earlier in the same season, and the variable sensed bythe in-situ sensors 308 may be a material consumption value. Thepredictive map 264 may then be a predictive material consumption mapthat maps predictive material consumption values to different geographiclocations in the field. In such an example, the relative materialconsumption value differences in the georeferenced information map 358from the prior year or earlier in the same season can be used bypredictive model generator 310 to generate a predictive model thatmodels a relationship between the relative material consumption valuedifferences on the information map 358 and the material consumptionvalues sensed by in-situ sensors 308 during the current operation. Thepredictive model is then used by predictive map generator 310 togenerate a predictive material consumption map. In another example, theinformation map 358 may be a speed map generated during a previous yearor earlier in the same season, and the variable sensed by the in-situsensors 308 may be a speed characteristic value. The predictive map 264may then be a predictive speed map that maps predictive speedcharacteristic values to different geographic locations in the field. Insuch an example, the relative speed characteristic value differences inthe georeferenced information map 358 from the prior year or earlier inthe same season can be used by predictive model generator 310 togenerate a predictive model that models a relationship between therelative speed characteristic value differences on the information map358 and the speed characteristic values sensed by in-situ sensors 308during the current operation. The predictive model is then used bypredictive map generator 310 to generate a predictive speed map.

In another example, the information map 358 may be a topographic mapgenerated during a prior operation in the same year and may maptopographic characteristic values to different geographic locations inthe field. The variable sensed by the in-situ sensors 308 during thecurrent operation may be a nutrient value, a weed value, a materialconsumption value, or a speed characteristic value. The predictive map264 may then map predictive characteristic values (e.g., nutrientvalues, weed values, material consumption values, or speedcharacteristic values) to different geographic locations in the field.In such an example, the topographic characteristic values at time of theprior operation are geo-referenced, recorded, and provided to mobilemachine 100 as an information map 358 of topographic characteristicvalues. In-situ sensors 308 during a current operation can detectcharacteristic values (e.g., nutrient values, weed values, materialconsumption values, or speed characteristic values) at geographiclocations in the field and predictive model generator 310 may then builda predictive model that models a relationship between characteristicvalues (e.g., nutrient values, weed values, material consumption values,or speed characteristic values) at the time of the current operation andtopographic characteristic values at the time of the prior operation.This is because the topographic characteristic values at the time of theprior operation are likely to be the same as at the time of the currentoperation or may be more accurate or otherwise may be more reliable (orfresher) than topographic characteristic values obtained in other ways.For instance, a machine that operated on the field previously mayprovide topographic characteristic values that are fresher (closer intime) or more accurate than topographic characteristic values detectedin other ways, such as satellite or other aerial-based sensing. Forinstance, vegetation on the field, meteorological conditions, as well asother obscurants, may obstruct or otherwise create noise that makestopographic characteristic values unavailable or unreliable. Thus, thetopographic map generated during the prior operation may be morepreferable. This is merely one example.

In some examples, predictive map 264 can be provided to the control zonegenerator 313. Control zone generator 313 groups adjacent portions of anarea into one or more control zones based on data values of predictivemap 264 that are associated with those adjacent portions. A control zonemay include two or more contiguous portions of a worksite, such as afield, for which a control parameter corresponding to the control zonefor controlling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 316 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator313 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems316. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 316 or for groups of controllable subsystems 316.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265.

It will also be appreciated that control zone generator 313 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay be used for controlling or calibrating mobile machine 100 or both.In other examples, the control zones may be presented to the operator360 and used to control or calibrate mobile machine 100, and, in otherexamples, the control zones may be presented to the operator 360 oranother user, such as a remote user 366, or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 314, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 329 controlscommunication system 306 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to other mobilemachines (e.g., delivery vehicles 379, other mobile material applicationmachines) that are operating at the same worksite or in the sameoperation. In some examples, communication system controller 329controls the communication system 306 to send the predictive map 264,predictive control zone map 265, or both to other remote systems, suchas remote computing systems 368 or delivery service systems 377, orboth.

Control system 314 includes communication system controller 329,interface controller 330, one or more material application controllers331, a propulsion controller 334, a path planning controller 335, one ormore zone controllers 336, logistics module 315, and control system 314can include other items 339. Controllable subsystems 316 includematerial application actuators 340, propulsion subsystem 350, steeringsubsystem 352, and can include a wide variety of other controllablesubsystems 356.

Control system 314 can control various items of agricultural system 300based on sensor data detected by sensors 308, models 311, predictive map264 or predictive map with control zones 265, operator or user inputs,as well as various other bases.

Interface controllers 330 are operable to generate control signals tocontrol interface mechanisms, such as operator interface mechanisms 318or user interface mechanisms 364, or both. While operator interfacemechanisms 318 are shown as separate from controllable subsystems 316,it will be understood that operator interface mechanisms 318 arecontrollable subsystems. The interface controllers 330 are also operableto present the predictive map 264 or predictive control zone map 265 orother information derived from or based on the predictive map 264,predictive control zone map 265, or both, to operator 360 or a remoteuser 366, or both. Operator 360 may be a local operator or a remoteoperator. As an example, interface controller 330 generates controlsignals to control a display mechanism to display one or both ofpredictive map 264 and predictive control zone map 265 for the operator360 or a remote user 366, or both. Interface controller 330 may generateoperator or user actuatable mechanisms that are displayed and can beactuated by the operator or user to interact with the displayed map. Theoperator or user can edit the map by, for example, correcting a valuedisplayed on the map, based on the operator's or the user's observation.

Propulsion controller 334 illustratively generates control signals tocontrol propulsion subsystem 350 to control a speed setting, such as oneor more of a travel speed, acceleration, deceleration, and direction(e.g., forward and reverse), such as based on one or more of thepredictive map 264 and the predictive control zone map 265 as well asbased on outputs from logistics module 315. Propulsion subsystem 350includes one or more powertrain components, such as a powerplant (e.g.,internal combustion engine, electric motors and batteries, etc.), atransmission or gear box, as well as various other items.

Path planning controller 335 illustratively generates control signals tocontrol steering subsystem 352 to steer mobile machine 100 according toa desired path or according to desired parameters, such as desiredsteering angles, based on one or more of the predictive map 264 and thepredictive control zone map 265 or logistics outputs from logisticsmodule 315. Path planning controller 333 can control a path planningsystem to generate a route for mobile machine 100 and can controlpropulsion subsystem 350 and steering subsystem 352 to steeragricultural mobile machine 100 along that route. Steering subsystem 352can include various items, such as one or more actuators to control anangle (steering angle) of one or more ground engaging elements (e.g.,wheels or tracks) of mobile machine 100.

Material application controllers 331 illustratively generate controlsignals, to control one or material application actuators 340 of mobilematerial application machine 100 to control the application of materialto the field, that is the amount of material applied, the rate at whichmaterial is applied, whether or not material is applied, etc. Materialapplication controllers 331 can generate control signals based on apredictive map 264 or predictive control zone map 265, or both. Materialapplication controllers 331 can generate control signals based onlogistics outputs from logistics module 315.

Material application actuators 340 can include a variety of differenttypes of actuators such as hydraulic, pneumatic, electromechanicalactuators, motors, pumps, valves, as well as various other types ofactuators. Some examples of actuators 340 are discussed above in FIGS.1-8. For example, actuators 340 can include actuators that drive thespeed of rotation of a seed meter, such as seed meter 124 or the speedof rotation of an assistive seed delivery, such as assistive seeddelivery system 166, or both. Actuators 340 can include actuators thatcontrol a flow of material from one or more material containers througha delivery line and to an outlet. For example, when material applicationmachine 100 is in the form of a planting machine, such as plantingmachine 100-1, actuators 340 can include one or more actuators, such aspumps or valves, or both, (e.g., 109 or 115) that control the flow ofmaterial from a material container (e.g., 107, 110, or 112) through adelivery line (e.g., 111) and out of an outlet (e.g., 119). In anotherexample, when material application machine 100 is a spraying machine,such as spraying machine 100-2 or 100-3, actuators can include one ormore pumps or one or more valves, or both, that control the flow ofmaterial from a material container (e.g., 234 or 255) through a conduitand out of an outlet (e.g., 230 or 258). In some examples, actuators 340may be controlled to activate or deactivate one or more components ofmaterial application machine 100. For example, actuators 340 may becontrolled to activate or deactivate one or more nozzles or one or moresections of a spraying machine.

In some examples, the mobile agricultural material application machine100 may have multiple material containers. Each container may contain adifferent variety of the material to be applied, for example, adifferent seed variety (e.g., genotype), a different fertilizer materialvariety, a different herbicide variety, a different pesticide variety,etc. In some examples, material application controllers 331 may controlmaterial application actuators 340 to control which variety is appliedto the field, based on a predictive map 264 or a predictive control zonemap 265, or both, as well as other inputs. For instance, it may bedesirable to plant weed resistant seed varieties in areas of the fieldwhere the map (264 or 265, or both) indicate high levels of weeds. Inanother example, it may be desirable to plant low nutrient requirementseed varieties in areas of the field where the map (264 or 265, or both)indicate low levels of nutrient(s). These are merely some examples. Inanother example, it may be desirable to apply a different variety ofherbicide in areas of the field where the map (264 or 265, or both)indicate weed of a given type, which may be resistant to the currentlyactivated herbicide. This is merely one example.

Zone controller 336 illustratively generates control signals to controlone or more controllable subsystems 316 to control operation of the oneor more controllable subsystems 316 based on the predictive control zonemap 265.

Logistics module 315 illustratively generates logistics control outputs.Logistics module 315 will be discussed in more detail in FIG. 16.

Other controllers 339 included on the mobile machine 100, or at otherlocations in agricultural system 300, can control other subsystems 356.

While the illustrated example of FIG. 10 shows that various componentsof agricultural system architecture 300 are located on mobile materialapplication machine 100, it will be understood that in other examplesone or more of the components illustrated on mobile material applicationmachine 100 in FIG. 10 can be located at other locations, such as one ormore remote computing systems 368. For instance, one or more of datastores 302, map selector 309, predictive model generator 310, predictivemodel 311, predictive map generator 312, functional predictive maps 263(e.g., 264 and 265), control zone generator 313, and control system 314(or components thereof) can be located remotely from mobile machine 100but can communicate with (or be communicated to) mobile machine 100 viacommunication system 306 and network 359. Thus, predictive models 311and functional predictive maps 263 may be generated and/or located atremote locations away from mobile machine 100 and can be communicated tomobile machine 100 over network 359, for instance, communication system306 can download the predictive models 311 and functional predictivemaps 263 from the remote locations and store them in data store 302. Inother examples, mobile machine 100 may access the predictive models 311and functional predictive maps 263 at the remote locations withoutdownloading the predictive models 311 and functional predictive maps263. The information used in the generation of the predictive models 311and functional predictive maps 263 may be provided to the predictivemodel generator 310 and the predictive map generator 312 at those remotelocations over network 359, for example in-situ sensor data generated byin-situ sensors 308 can be provided over network 359 to the remotelocations. Similarly, information maps 358 can be provided to the remotelocations.

In some examples, control system 314 may remain local to mobile machine100, and a remote system (e.g., 368 or 364) may be provided withfunctionality (e.g., such as a control signal generator) thatcommunicates control commands to mobile machine 100 that are used bycontrol system 314 for the control of mobile machine 100.

Similarly, where various components are located remotely from mobilemachine 100, those components can receive data from components of mobilemachine 100 over network 359. For example, where predictive modelgenerator 310 and predictive map generator 312 are located remotely frommobile machine 100, such as at remote computing systems 368, datagenerated by in-situ sensors 308 and geographic position sensors 304,for instance, can be communicated to the remote computing systems 368over network 359. Additionally, information maps 358 can be obtained byremote computing systems 368 over network 359 or over another network.

FIG. 11 is a block diagram of a portion of the agricultural materialapplication system architecture 300 shown in FIG. 10. Particularly, FIG.11 shows, among other things, examples of the predictive model generator310 and the predictive map generator 312 in more detail. FIG. 11 alsoillustrates information flow among the various components shown. Thepredictive model generator 310 receives one or more information map(s)358. In the example illustrated in FIG. 11, information maps 358 includeone or more of a soil property map 410, a yield map 411, a residue map412, a constituents map 413, a seeding map 414, a topographic map 415, avegetative index map 416, or any of a wide variety of other informationmaps 429. Predictive model generator 310 also receives geographiclocation data 434, such as an indication of a geographic location, fromgeographic position sensor 304. Geographic location data 434illustratively represents the geographic locations to which valuesdetected by in-situ sensors 308 correspond. In some examples, thegeographic position of the mobile machine 100, as detected by geographicposition sensors 304, will not be the same as the geographic position onthe field to which a value detected by in-situ sensors 308 corresponds.It will be appreciated, that the geographic position indicated bygeographic position sensor 304, along with timing, machine speed andheading, machine dimensions, machine processing delays, sensor position(e.g., relative to geographic position sensor), sensor parameters (e.g.,sensor field of view), as well as various other data, can be used toderive a geographic location at the field to which a value a detected byan in-situ sensor 308 corresponds.

In-situ sensors 308 illustratively include nutrient sensors 374, as wellas processing system 338. In some examples, processing system 338 isseparate from in-situ sensors 308 (such as the example shown in FIG.10). In some instances, nutrient sensors 374 may be located on-boardmobile material application machine 100. The processing system 338processes sensor data generated from nutrient sensors 374 to generateprocessed sensor data 430 indicative of nutrient values (e.g., soilnutrient values or plant nutrient values, or both).

As shown in FIG. 11, the example predictive model generator 310 includesa nutrient-to-soil property model generator 440, a nutrient-to-yieldmodel generator 441, a nutrient-to-residue model generator 442, anutrient-to-constituents model generator 443, a nutrient-to-seedingcharacteristic model generator 444, a nutrient-to-topographiccharacteristic model generator 445, a nutrient-to-vegetative index modelgenerator 446, and a nutrient-to-other characteristic model generator448. In other examples, the predictive model generator 310 may includeadditional, fewer, or different components than those shown in theexample of FIG. 11. Consequently, in some examples, the predictive modelgenerator 310 may include other items 449 as well, which may includeother types of predictive model generators to generate other types ofnutrient models.

Nutrient-to-soil property model generator 440 identifies a relationshipbetween nutrient value(s) detected in in-situ sensor data 430, atgeographic location(s) to which the nutrient value(s), detected in thein-situ sensor data 430, correspond, and soil property value(s) from thesoil property map 410 corresponding to the same geographic location(s)to which the detected nutrient value(s) correspond. Based on thisrelationship established by nutrient-to-soil property model generator440, nutrient-to-soil property model generator 440 generates apredictive nutrient model. The predictive nutrient model is used bypredictive nutrient map generator 452 to predict a nutrient value atdifferent locations in the field based upon the georeferenced soilproperty values contained in the soil property map 410 corresponding tothe same locations in the field. Thus, for a given location in thefield, a nutrient value can be predicted at the given location based onthe predictive nutrient model and the soil property value, from the soilproperty map 415, corresponding to that given location.

Nutrient-to-yield model generator 441 identifies a relationship betweennutrient value(s) detected in in-situ sensor data 430, at geographiclocation(s) to which the nutrient value(s), detected in the in-situsensor data 430, correspond, and yield value(s) from the yield map 411corresponding to the same geographic location(s) to which the detectednutrient value(s) correspond. Based on this relationship established bynutrient-to-yield model generator 441, nutrient-to-yield model generator441 generates a predictive nutrient model. The predictive nutrient modelis used by predictive nutrient map generator 452 to predict a nutrientvalue at different locations in the field based upon the georeferencedyield values contained in the yield map 411 corresponding to the samelocations in the field. Thus, for a given location in the field, anutrient value can be predicted at the given location based on thepredictive nutrient model and the yield value, from the yield map 411,corresponding to that given location.

Nutrient-to-residue model generator 442 identifies a relationshipbetween nutrient value(s) detected in in-situ sensor data 430, atgeographic location(s) to which the nutrient value(s), detected in thein-situ sensor data 430, correspond, and residue value(s) from theresidue map 412 corresponding to the same geographic location(s) towhich the detected nutrient value(s) correspond. Based on thisrelationship established by nutrient-to-residue model generator 442,nutrient-to-residue model generator 442 generates a predictive nutrientmodel. The predictive nutrient model is used by predictive nutrient mapgenerator 452 to predict a nutrient value at different locations in thefield based upon the georeferenced residue values contained in theresidue map 412 corresponding to the same locations in the field. Thus,for a given location in the field, a nutrient value can be predicted atthe given location based on the predictive nutrient model and theresidue value, from the residue map 412, corresponding to that givenlocation.

Nutrient-to-constituents model generator 443 identifies a relationshipbetween nutrient value(s) detected in in-situ sensor data 430, atgeographic location(s) to which the nutrient value(s), detected in thein-situ sensor data 430, correspond, and constituent value(s) from theconstituents map 413 corresponding to the same geographic location(s) towhich the detected nutrient value(s) correspond. Based on thisrelationship established by nutrient-to-constituents model generator443, nutrient-to-constituents model generator 443 generates a predictivenutrient model. The predictive nutrient model is used by predictivenutrient map generator 452 to predict a nutrient value at differentlocations in the field based upon the georeferenced constituent valuescontained in the constituents map 413 corresponding to the samelocations in the field. Thus, for a given location in the field, anutrient value can be predicted at the given location based on thepredictive nutrient model and the constituent value, from theconstituents map 413, corresponding to that given location.

Nutrient-to-seeding characteristic model generator 444 identifies arelationship between nutrient value(s) detected in in-situ sensor data430, at geographic location(s) to which the nutrient value(s), detectedin the in-situ sensor data 430, correspond, and seeding characteristicvalue(s) from the seeding map 414 corresponding to the same geographiclocation(s) to which the detected nutrient value(s) correspond. Based onthis relationship established by nutrient-to-seeding characteristicmodel generator 444, nutrient-to-seeding characteristic model generator444 generates a predictive nutrient model. The predictive nutrient modelis used by predictive nutrient map generator 452 to predict a nutrientvalue at different locations in the field based upon the georeferencedseeding characteristic values contained in the seeding map 414corresponding to the same locations in the field. Thus, for a givenlocation in the field, a nutrient value can be predicted at the givenlocation based on the predictive nutrient model and the seedingcharacteristic value, from the seeding map 414, corresponding to thatgiven location.

Nutrient-to-topographic characteristic model generator 445 identifies arelationship between nutrient value(s) detected in in-situ sensor data430, at geographic location(s) to which the nutrient value(s), detectedin the in-situ sensor data 430, correspond, and topographiccharacteristic value(s) from the topographic map 415 corresponding tothe same geographic location(s) to which the detected nutrient value(s)correspond. Based on this relationship established bynutrient-to-topographic characteristic model generator 445,nutrient-to-topographic characteristic model generator 445 generates apredictive nutrient model. The predictive nutrient model is used bypredictive nutrient map generator 452 to predict a nutrient value atdifferent locations in the field based upon the georeferencedtopographic characteristic values contained in the topographic map 415corresponding to the same locations in the field. Thus, for a givenlocation in the field, a nutrient value can be predicted at the givenlocation based on the predictive nutrient model and the topographiccharacteristic value, from the topographic map 415, corresponding tothat given location.

Nutrient-to-vegetative index model generator 446 identifies arelationship between nutrient value(s) detected in in-situ sensor data430, at geographic location(s) to which the nutrient value(s), detectedin the in-situ sensor data 430, correspond, and vegetative indexvalue(s) from the vegetative index map 416 corresponding to the samegeographic location(s) to which the detected nutrient value(s)correspond. Based on this relationship established bynutrient-to-vegetative index model generator 446, nutrient-to-vegetativeindex model generator 446 generates a predictive nutrient model. Thepredictive nutrient model is used by predictive nutrient map generator452 to predict a nutrient value at different locations in the fieldbased upon the georeferenced vegetative index values contained in thevegetative index map 416 corresponding to the same locations in thefield. Thus, for a given location in the field, a nutrient value can bepredicted at the given location based on the predictive nutrient modeland the vegetative index value, from the vegetative index map 416,corresponding to that given location.

Nutrient-to-other characteristic model generator 448 identifies arelationship between nutrient value(s) detected in in-situ sensor data430, at geographic location(s) to which the nutrient value(s), detectedin the in-situ sensor data 430, correspond, and other characteristicvalue(s) from an other map 429 corresponding to the same geographiclocation(s) to which the detected nutrient value(s) correspond. Based onthis relationship established by nutrient-to-other characteristic modelgenerator 448, nutrient-to-other characteristic model generator 448generates a predictive nutrient model. The predictive nutrient model isused by predictive nutrient map generator 452 to predict a nutrientvalue at different locations in the field based upon the georeferencedother characteristic values contained in the other map 429 correspondingto the same locations in the field. Thus, for a given location in thefield, a nutrient value can be predicted at the given location based onthe predictive nutrient model and the other characteristic value, fromthe other map 415, corresponding to that given location.

In light of the above, the predictive model generator 310 is operable toproduce a plurality of predictive nutrient models, such as one or moreof the predictive nutrient models generated by model generators 440,441, 442, 443, 444, 445, 446, 448, and 449. In another example, two ormore of the predictive models described above may be combined into asingle predictive nutrient model, such as a predictive nutrient modelthat predicts a nutrient value based upon two or more of the soilproperty values, the yield values, the residue values, the constituentvalues, the seeding characteristic values, the topographiccharacteristic values, the vegetative index values, and the othercharacteristic values at different locations in the field. Any of thesenutrient models, or combinations thereof, are represented collectivelyby predictive nutrient model 450 in FIG. 11.

The predictive nutrient model 450 is provided to predictive mapgenerator 312. In the example of FIG. 11, predictive map generator 312includes a predictive nutrient map generator 452. In other examples,predictive map generator 312 may include additional or different mapgenerators. Thus, in some examples, predictive map generator 312 mayinclude other items 456 which may include other types of map generatorsto generate other types of maps.

Predictive nutrient map generator 452 receives one or more of the soilproperty map 410, the yield map 411, the residue map 412, theconstituents map 413, the seeding map 414, the topographic map 415, andan other map 429, along with the predictive nutrient model 450 whichpredicts a nutrient value based upon one or more of a soil propertyvalue, a yield value, a residue value, a constituent value, a seedingcharacteristic value, a topographic characteristic value, a vegetativeindex value, and an other characteristic value, and generates apredictive map that predicts a nutrient value at different locations inthe field, such as functional predictive nutrient map 460.

Predictive map generator 312 thus outputs a functional predictivenutrient map 460 that is predictive of a nutrient value. The functionalpredictive nutrient map 460 is a predictive map 264. The functionalpredictive nutrient map 460 predicts a nutrient value at differentlocations in a field. The functional predictive nutrient map 460 may beprovided to control zone generator 313, control system 314, or both.Control zone generator 313 generates control zones and incorporatesthose control zones into the functional predictive nutrient map 460 toproduce a predictive control zone map 265, that is a functionalpredictive nutrient control zone map 461. One or both of functionalpredictive nutrient map 460 and functional predictive nutrient controlzone map 461 may be provided to control system 314, which generatescontrol signals to control one or more of the controllable subsystems316 based upon the functional predictive nutrient map 460, thefunctional predictive nutrient control zone map 461, or both.

FIG. 12 is a block diagram of a portion of the agricultural materialapplication system architecture 300 shown in FIG. 10. Particularly, FIG.12 shows, among other things, examples of the predictive model generator310 and the predictive map generator 312 in more detail. FIG. 12 alsoillustrates information flow among the various components shown. Thepredictive model generator 310 receives one or more information map(s)358. In the example illustrated in FIG. 12, information maps 358 includeone or more of a vegetative index map 416, an optical map 417, a weedmap 418, or any of a wide variety of other information maps 429.Predictive model generator 310 also receives geographic location data1434, such as an indication of a geographic location, from geographicposition sensor 304. Geographic location data 1434 illustrativelyrepresents the geographic locations to which values detected by in-situsensors 308 correspond. In some examples, the geographic position of themobile machine 100, as detected by geographic position sensors 304, willnot be the same as the geographic position on the field to which a valuedetected by in-situ sensors 308 corresponds. It will be appreciated,that the geographic position indicated by geographic position sensor304, along with timing, machine speed and heading, machine dimensions,machine processing delays, sensor position (e.g., relative to geographicposition sensor), sensor parameters (e.g., sensor field of view), aswell as various other data, can be used to derive a geographic locationat the field to which a value a detected by an in-situ sensor 308corresponds.

In-situ sensors 308 illustratively include weed sensors 372, as well asprocessing system 338. In some examples, processing system 338 isseparate from in-situ sensors 308 (such as the example shown in FIG.10). In some instances, weed sensors 372 may be located on-board mobilematerial application machine 100. The processing system 338 processessensor data generated from weed sensors 372 to generate processed sensordata 1430 indicative of weed values. Weed sensors 372 and processingsystem 338 may sense one or more visual properties of weeds includingcolor, size, and shape. Weed sensors 372 and processing system 338 maysense plant locations relative to known crop seed or plant locations orto known weed seed or plant locations.

As shown in FIG. 12, the example predictive model generator 310 includesa weed-to-vegetative index model generator 1440, a weed-to-opticalcharacteristic model generator 1441, a weed-to-weed model generator1442, and a weed-to-other characteristic model generator 1445. In otherexamples, the predictive model generator 310 may include additional,fewer, or different components than those shown in the example of FIG.12. Consequently, in some examples, the predictive model generator 310may include other items 1449 as well, which may include other types ofpredictive model generators to generate other types of weed models.

Weed-to-vegetative index model generator 1440 identifies a relationshipbetween weed value(s) detected in in-situ sensor data 1430, atgeographic location(s) to which the weed value(s), detected in thein-situ sensor data 1430, correspond, and vegetative index value(s) fromthe vegetative index map 416 corresponding to the same geographiclocation(s) to which the detected weed value(s) correspond. Based onthis relationship established by weed-to-vegetative index modelgenerator 1440, weed-to-vegetative index model generator 1440 generatesa predictive weed model. The predictive weed model is used by predictiveweed map generator 1452 to predict a weed value at different locationsin the field based upon the georeferenced vegetative index valuescontained in the vegetative index map 416 corresponding to the samelocations in the field. Thus, for a given location in the field, a weedvalue can be predicted at the given location based on the predictiveweed model and the vegetative index value, from the vegetative index map416, corresponding to that given location.

Weed-to-optical characteristic model generator 1441 identifies arelationship between weed value(s) detected in in-situ sensor data 1430,at geographic location(s) to which the weed value(s), detected in thein-situ sensor data 1430, correspond, and optical characteristicvalue(s) from the optical map 417 corresponding to the same geographiclocation(s) to which the detected weed value(s) correspond. Based onthis relationship established by weed-to-optical characteristic modelgenerator 1441, weed-to-optical characteristic model generator 1441generates a predictive weed model. The predictive weed model is used bypredictive weed map generator 1452 to predict a weed value at differentlocations in the field based upon the georeferenced opticalcharacteristic values contained in the optical map 417 corresponding tothe same locations in the field. Thus, for a given location in thefield, a weed value can be predicted at the given location based on thepredictive weed model and the optical characteristic value, from theoptical map 417, corresponding to that given location.

Weed-to-weed model generator 1442 identifies a relationship between weedvalue(s) detected in in-situ sensor data 1430, at geographic location(s)to which the weed value(s), detected in the in-situ sensor data 1430,correspond, and weed value(s) from the weed map 418 corresponding to thesame geographic location(s) to which the detected weed value(s)correspond. Based on this relationship established by weed-to-weed modelgenerator 1442, weed-to-weed model generator 1442 generates a predictiveweed model. The predictive weed model is used by predictive weed mapgenerator 1452 to predict a weed value at different locations in thefield based upon the georeferenced weed values contained in the weed map418 corresponding to the same locations in the field. Thus, for a givenlocation in the field, a weed value can be predicted at the givenlocation based on the predictive weed model and the weed value, from theweed map 418, corresponding to that given location.

Weed-to-other characteristic model generator 1445 identifies arelationship between weed value(s) detected in in-situ sensor data 1430,at geographic location(s) to which the weed value(s), detected in thein-situ sensor data 1430, correspond, and other characteristic value(s)from an other map 429 corresponding to the same geographic location(s)to which the detected weed value(s) correspond. Based on thisrelationship established by weed-to-other characteristic model generator1445, weed-to-other characteristic model generator 1445 generates apredictive weed model. The predictive weed model is used by predictiveweed map generator 1452 to predict a weed value at different locationsin the field based upon the georeferenced other characteristic valuescontained in the other map 429 corresponding to the same locations inthe field. Thus, for a given location in the field, a weed value can bepredicted at the given location based on the predictive weed model andthe other characteristic value, from the other map 429, corresponding tothat given location.

In light of the above, the predictive model generator 310 is operable toproduce a plurality of predictive weed models, such as one or more ofthe predictive weed models generated by model generators 1440, 1441,1442, 1445, and 1449. In another example, two or more of the predictivemodels described above may be combined into a single predictive weedmodel, such as a predictive weed model that predicts a weed value basedupon two or more of the vegetative index values, the opticalcharacteristic values, the weed values, and the other characteristicvalues at different locations in the field. Any of these weed models, orcombinations thereof, are represented collectively by predictive weedmodel 1450 in FIG. 12.

The predictive weed model 1450 is provided to predictive map generator312. In the example of FIG. 12, predictive map generator 312 includes apredictive weed map generator 1452. In other examples, predictive mapgenerator 312 may include additional or different map generators. Thus,in some examples, predictive map generator 312 may include other items1456 which may include other types of map generators to generate othertypes of maps.

Predictive weed map generator 1452 receives one or more of thevegetative index map 416, the optical map 417, the weed map 418, and another map 429, along with the predictive weed model 1450 which predictsa weed value based upon one or more of a vegetative index value, anoptical characteristic value, a weed value, and an other characteristicvalue, and generates a predictive map that predicts a weed value atdifferent locations in the field, such as functional predictive weed map1460.

Predictive map generator 312 thus outputs a functional predictive weedmap 1460 that is predictive of a weed value. The functional predictiveweed map 1460 is a predictive map 264. The functional predictive weedmap 1460 predicts a weed value at different locations in a field. Thefunctional predictive weed map 1460 may be provided to control zonegenerator 313, control system 314, or both. Control zone generator 313generates control zones and incorporates those control zones into thefunctional predictive weed map 1460 to produce a predictive control zonemap 265, that is a functional predictive weed control zone map 1461. Oneor both of functional predictive weed map 1460 and functional predictiveweed control zone map 1461 may be provided to control system 314, whichgenerates control signals to control one or more of the controllablesubsystems 316 based upon the functional predictive weed map 1460, thefunctional predictive weed control zone map 1461, or both.

FIG. 13 is a block diagram of a portion of the agricultural materialapplication system architecture 300 shown in FIG. 10. Particularly, FIG.13 shows, among other things, examples of the predictive model generator310 and the predictive map generator 312 in more detail. FIG. 13 alsoillustrates information flow among the various components shown. Thepredictive model generator 310 receives one or more information map(s)358. In the example illustrated in FIG. 13, information maps 358 includeone or more of a soil property map 410, a topographic map 415,vegetative index map 416, a weed map 418, a contamination map 419, orany of a wide variety of other information maps 429. Predictive modelgenerator 310 also receives geographic location data 2434, such as anindication of a geographic location, from geographic position sensor304. Geographic location data 2434 illustratively represents thegeographic locations to which values detected by in-situ sensors 308correspond. In some examples, the geographic position of the mobilemachine 100, as detected by geographic position sensors 304, will not bethe same as the geographic position on the field to which a valuedetected by in-situ sensors 308 corresponds. It will be appreciated,that the geographic position indicated by geographic position sensor304, along with timing, machine speed and heading, machine dimensions,machine processing delays, sensor position (e.g., relative to geographicposition sensor), sensor parameters (e.g., sensor field of view), aswell as various other data, can be used to derive a geographic locationat the field to which a value a detected by an in-situ sensor 308corresponds.

In-situ sensors 308 illustratively include material consumption sensors376, as well as processing system 338. In some examples, processingsystem 338 is separate from in-situ sensors 308 (such as the exampleshown in FIG. 10). In some instances, material consumption sensors 376may be located on-board mobile material application machine 100. Theprocessing system 338 processes sensor data generated from materialconsumption sensors 376 to generate processed sensor data 2430indicative of material consumption values.

As shown in FIG. 13, the example predictive model generator 310 includesa material consumption-to-soil property model generator 2440, a materialconsumption-to-topographic characteristic model generator 2441, amaterial consumption-to-vegetative index model generator 2442, amaterial consumption-to-weed model generator 2443, a materialconsumption-to-contamination model generator 2444, and a materialconsumption-to-other characteristic model generator 2445. In otherexamples, the predictive model generator 310 may include additional,fewer, or different components than those shown in the example of FIG.13. Consequently, in some examples, the predictive model generator 310may include other items 2449 as well, which may include other types ofpredictive model generators to generate other types of materialconsumption models.

Material consumption-to-soil property model generator 2440 identifies arelationship between material consumption value(s) detected in in-situsensor data 2430, at geographic location(s) to which the materialconsumption value(s), detected in the in-situ sensor data 2430,correspond, and soil property value(s) from the soil property map 410corresponding to the same geographic location(s) to which the detectedmaterial consumption value(s) correspond. Based on this relationshipestablished by material consumption-to-soil property model generator2440, material consumption-to-soil property model generator 2440generates a predictive material consumption model. The predictivematerial consumption model is used by predictive material consumptionmap generator 2452 to predict a material consumption value at differentlocations in the field based upon the georeferenced soil property valuescontained in the soil property map 410 corresponding to the samelocations in the field. Thus, for a given location in the field, amaterial consumption value can be predicted at the given location basedon the predictive material consumption model and the soil propertyvalue, from the soil property map 410, corresponding to that givenlocation.

Material consumption-to-topographic characteristic model generator 2441identifies a relationship between material consumption value(s) detectedin in-situ sensor data 2430, at geographic location(s) to which thematerial consumption value(s), detected in the in-situ sensor data 2430,correspond, and topographic characteristic value(s) from the topographicmap 415 corresponding to the same geographic location(s) to which thedetected material consumption value(s) correspond. Based on thisrelationship established by material consumption-to-topographiccharacteristic model generator 2441, material consumption-to-topographiccharacteristic model generator 2441 generates a predictive materialconsumption model. The predictive material consumption model is used bypredictive material consumption map generator 2452 to predict a materialconsumption value at different locations in the field based upon thegeoreferenced topographic characteristic values contained in thetopographic map 415 corresponding to the same locations in the field.Thus, for a given location in the field, a material consumption valuecan be predicted at the given location based on the predictive materialconsumption model and the topographic characteristic value, from thetopographic map 415, corresponding to that given location.

Material consumption-to-vegetative index model generator 2442 identifiesa relationship between material consumption value(s) detected in in-situsensor data 2430, at geographic location(s) to which the materialconsumption value(s), detected in the in-situ sensor data 2430,correspond, and vegetative index value(s) from the vegetative index map416 corresponding to the same geographic location(s) to which thedetected material consumption value(s) correspond. Based on thisrelationship established by material consumption-to-vegetative indexmodel generator 2442, material consumption-to-vegetative index modelgenerator 2442 generates a predictive material consumption model. Thepredictive material consumption model is used by predictive materialconsumption map generator 2452 to predict a material consumption valueat different locations in the field based upon the georeferencedvegetative index values contained in the vegetative index map 416corresponding to the same locations in the field. Thus, for a givenlocation in the field, a material consumption value can be predicted atthe given location based on the predictive material consumption modeland the vegetative index value, from the vegetative index map 416,corresponding to that given location.

Material consumption-to-weed model generator 2443 identifies arelationship between material consumption value(s) detected in in-situsensor data 2430, at geographic location(s) to which the materialconsumption value(s), detected in the in-situ sensor data 2430,correspond, and weed value(s) from the weed map 418 corresponding to thesame geographic location(s) to which the detected material consumptionvalue(s) correspond. Based on this relationship established by materialconsumption-to-weed model generator 2443, material consumption-to-weedmodel generator 2442 generates a predictive material consumption model.The predictive material consumption model is used by predictive materialconsumption map generator 2452 to predict a material consumption valueat different locations in the field based upon the georeferenced weedvalues contained in the weed map 418 corresponding to the same locationsin the field. Thus, for a given location in the field, a materialconsumption value can be predicted at the given location based on thepredictive material consumption model and the weed value, from the weedmap 418, corresponding to that given location.

Material consumption-to-contamination model generator 2444 identifies arelationship between material consumption value(s) detected in in-situsensor data 2430, at geographic location(s) to which the materialconsumption value(s), detected in the in-situ sensor data 2430,correspond, and contamination value(s) from the contamination map 419corresponding to the same geographic location(s) to which the detectedmaterial consumption value(s) correspond. Based on this relationshipestablished by material consumption-to-contamination model generator2444, material consumption-to-contamination model generator 2444generates a predictive material consumption model. The predictivematerial consumption model is used by predictive material consumptionmap generator 2452 to predict a material consumption value at differentlocations in the field based upon the georeferenced contamination valuescontained in the contamination map 419 corresponding to the samelocations in the field. Thus, for a given location in the field, amaterial consumption value can be predicted at the given location basedon the predictive material consumption model and the contaminationvalue, from the contamination map 419, corresponding to that givenlocation.

Material consumption-to-other characteristic model generator 2445identifies a relationship between material consumption value(s) detectedin in-situ sensor data 2430, at geographic location(s) to which thematerial consumption value(s), detected in the in-situ sensor data 2430,correspond, and other characteristic value(s) from an other map 429corresponding to the same geographic location(s) to which the detectedmaterial consumption value(s) correspond. Based on this relationshipestablished by material consumption-to-other characteristic modelgenerator 2445, material consumption-to-other characteristic modelgenerator 2445 generates a predictive material consumption model. Thepredictive material consumption model is used by predictive materialconsumption map generator 2452 to predict a material consumption valueat different locations in the field based upon the georeferenced othercharacteristic values contained in the other map 429 corresponding tothe same locations in the field. Thus, for a given location in thefield, a material consumption value can be predicted at the givenlocation based on the predictive material consumption model and theother characteristic value, from the other map 429, corresponding tothat given location.

In light of the above, the predictive model generator 310 is operable toproduce a plurality of predictive material consumption models, such asone or more of the predictive material consumption models generated bymodel generators 2440, 2441, 2442, 2443, 2444, 2445, and 2449. Inanother example, two or more of the predictive models described abovemay be combined into a single predictive material consumption model,such as a predictive material consumption model that predicts a materialconsumption value based upon two or more of the soil property values,the topographic characteristic values, the vegetative index values, theweed values, the contamination values, and the other characteristicvalues at different locations in the field. Any of these materialconsumption models, or combinations thereof, are representedcollectively by predictive material consumption model 2450 in FIG. 13.

The predictive material consumption model 2450 is provided to predictivemap generator 312. In the example of FIG. 13, predictive map generator312 includes a predictive material consumption map generator 2452. Inother examples, predictive map generator 312 may include additional ordifferent map generators. Thus, in some examples, predictive mapgenerator 312 may include other items 2456 which may include other typesof map generators to generate other types of maps.

Predictive material consumption map generator 2452 receives one or moreof the soil property map 410, the topographic map 415, the vegetativeindex map 416, the weed map 418, the contamination map 419, and an othermap 429, along with the predictive material consumption model 2450 whichpredicts a material consumption value based upon one or more of a soilproperty value, a topographic characteristic value, a vegetative indexvalue, a weed value, a contamination value, and an other characteristicvalue, and generates a predictive map that predicts a materialconsumption value at different locations in the field, such asfunctional predictive material consumption map 2460.

Predictive map generator 312 thus outputs a functional predictivematerial consumption map 2460 that is predictive of a materialconsumption value. The functional predictive material consumption map2460 is a predictive map 264. The functional predictive materialconsumption map 2460 predicts a material consumption value at differentlocations in a field. The functional predictive material consumption map2460 may be provided to control zone generator 313, control system 314,or both. Control zone generator 313 generates control zones andincorporates those control zones into the functional predictive materialconsumption map 2460 to produce a predictive control zone map 265, thatis a functional predictive material consumption control zone map 2461.One or both of functional predictive material consumption map 2460 andfunctional predictive material consumption control zone map 2461 may beprovided to control system 314, which generates control signals tocontrol one or more of the controllable subsystems 316 based upon thefunctional predictive material consumption map 2460, the functionalpredictive material consumption control zone map 2461, or both.

FIG. 14 is a block diagram of a portion of the agricultural materialapplication system architecture 300 shown in FIG. 10. Particularly, FIG.14 shows, among other things, examples of the predictive model generator310 and the predictive map generator 312 in more detail. FIG. 14 alsoillustrates information flow among the various components shown. Thepredictive model generator 310 receives one or more information map(s)358. In the example illustrated in FIG. 14, information maps 358 caninclude one or more of the information maps 358 discussed herein, aswell as various other types of information maps. Predictive modelgenerator 310 also receives geographic location data 3434, such as anindication of a geographic location, from geographic position sensor304. Geographic location data 3434 illustratively represents thegeographic locations to which values detected by in-situ sensors 308correspond. In some examples, the geographic position of the mobilemachine 100, as detected by geographic position sensors 304, will not bethe same as the geographic position on the field to which a valuedetected by in-situ sensors 308 corresponds. It will be appreciated,that the geographic position indicated by geographic position sensor304, along with timing, machine speed and heading, machine dimensions,machine processing delays, sensor position (e.g., relative to geographicposition sensor), sensor parameters (e.g., sensor field of view), aswell as various other data, can be used to derive a geographic locationat the field to which a value a detected by an in-situ sensor 308corresponds.

In-situ sensors 308 illustratively include heading/speed sensors 325, aswell as processing system 338. In some examples, processing system 338is separate from in-situ sensors 308 (such as the example shown in FIG.10). In some instances, heading/speed sensors 325 may be locatedon-board mobile material application machine 100. The processing system338 processes sensor data generated from heading/speed sensors 325 togenerate processed sensor data 3430 indicative of speed characteristicvalues.

As shown in FIG. 14, the example predictive model generator 310 includesa speed characteristic-to-mapped characteristic(s) model generator 2440.In other examples, the predictive model generator 310 may includeadditional, fewer, or different components than those shown in theexample of FIG. 14. Consequently, in some examples, the predictive modelgenerator 310 may include other items 3449 as well, which may includeother types of predictive model generators to generate other types ofspeed models.

Speed characteristic-to-mapped characteristic(s) model generator 3440identifies a relationship between speed characteristic value(s) detectedin in-situ sensor data 3430, at geographic location(s) to which thespeed characteristic value(s), detected in the in-situ sensor data 3430,correspond, and value(s) of one or more characteristics from the one ormore information maps 358 corresponding to the same geographiclocation(s) to which the detected speed characteristic value(s)correspond. Based on this relationship established by speedcharacteristic-to-mapped characteristic(s) model generator 3440, speedcharacteristic-to-mapped characteristic(s) model generator 3440generates a predictive speed model. The predictive speed model is usedby predictive speed map generator 3452 to predict a speed characteristicvalue at different locations in the field based upon the georeferencedvalues of the one or more characteristics contained in the one or moreinformation maps 358 corresponding to the same locations in the field.Thus, for a given location in the field, a speed characteristic valuecan be predicted at the given location based on the predictive speedmodel and the value of one or more characteristics, from the one or moreinformation maps 358, corresponding to that given location.

In light of the above, the predictive model generator 310 is operable toproduce a plurality of predictive speed models, such as one or more ofthe predictive speed models generated by model generators 3440 and 3449.In another example, two or more of the predictive models described abovemay be combined into a single predictive speed model, such as apredictive speed model that predicts a speed characteristic value basedupon values of two or more characteristics at different locations in thefield. Any of these speed models, or combinations thereof, arerepresented collectively by predictive speed model 3450 in FIG. 14.

The predictive speed model 3450 is provided to predictive map generator312. In the example of FIG. 14, predictive map generator 312 includes apredictive speed map generator 3452. In other examples, predictive mapgenerator 312 may include additional or different map generators. Thus,in some examples, predictive map generator 312 may include other items3456 which may include other types of map generators to generate othertypes of maps.

Predictive speed map generator 3452 receives one or more of theinformation maps 358, along with the predictive speed model 3450 whichpredicts a speed characteristic value based upon a value of one or morecharacteristics, and generates a predictive map that predicts a speedcharacteristic value at different locations in the field, such asfunctional predictive speed map 3460.

Predictive map generator 312 thus outputs a functional predictive speedmap 3460 that is predictive of a speed characteristic value. Thefunctional predictive speed map 3460 is a predictive map 264. Thefunctional predictive speed map 3460 predicts a speed characteristicvalue at different locations in a field. The functional predictive speedmap 3460 may be provided to control zone generator 313, control system314, or both. Control zone generator 313 generates control zones andincorporates those control zones into the functional predictive speedmap 3460 to produce a predictive control zone map 265, that is afunctional predictive speed control zone map 3461. One or both offunctional predictive speed map 3460 and functional predictive speedcontrol zone map 3461 may be provided to control system 314, whichgenerates control signals to control one or more of the controllablesubsystems 316 based upon the functional predictive speed map 3460, thefunctional predictive speed control zone map 3461, or both.

In some cases, where the mobile machine 100 is to be controlled based ona functional predictive map or a functional predictive control zone map,or both, multiple target settings for the same actuator may be possibleat a given location. In that case, the target settings may havedifferent values and may be competing. Thus, the target settings need tobe resolved so that only a single target setting is used to control theactuators. For example, where the actuator is an actuator in propulsionsubsystem 350 that is being controlled in order to control the speed ofmobile machine 100, there may be multiple target speed settings. In sucha case, control zone generator 313 may select one of the competingtarget settings to control the mobile machine. Thus, in generating thefunctional predictive control zone map that is eventually provided tothe control system, operator, or user, for control of the mobilemachine, control zone generator 313 may first resolve competing targetsettings of competing control zones. Control zone generator 313 mayselect the competing settings based on a number of criteria, forexample, various performance metrics such as time to complete, jobquality, fuel cost, labor cost, etc. may be used. There may be ahierarchy of these criteria which can be selectively adjusted, such asbased on operator or user input, or based on default rankings. As anexample, time to complete may be input as the highest priority in thehierarchy, and thus the target setting corresponding to time to completewill be selected. This is merely one example. In other examples,characteristics of the information maps 358 used in the generation ofthe functional predictive map and functional predictive control zone mapmay have a priority or hierarchy. For example, target settings based onyield values from a yield map may have a higher priority than targetsettings based on topographic values from a topographic map, and thus,the value corresponding to the yield value will be selected over thevalues corresponding to the topographic value. This is merely oneexample. In either case, it will be understood that control zonegenerator 313 can resolve competing target settings such that thecontrol zone map that is generated and provided for control does notcontain competing target settings.

FIGS. 15A-15B (collectively referred to herein as FIG. 15) show a flowdiagram illustrating one example of the operation of agriculturalmaterial application architecture 300 in generating a predictive modeland a predictive map.

At block 502, agricultural material application system 300 receives oneor more information maps 358. Examples of information maps 358 orreceiving information maps 358 are discussed with respect to blocks 504,505, 507, and 508. As discussed above, information maps 358 map valuesof a variable, corresponding to a characteristic, to different locationsin the worksite, as indicated at block 505. As indicated at block 504,receiving the information maps 358 may involve selecting one or more ofa plurality of possible information maps 358 that are available. Forinstance, one information map 358 may be a soil property map, such assoil property map 410. Another information map 358 may be a yield map,such as yield map 411. Another information map 358 may be a residue map,such as residue map 412. Another information map 358 may be aconstituents map, such as constituents map 413. Another information map358 may be a seeding map, such as seeding map 414. Another informationmap 358 may be a topographic map, such as topographic map 415. Anotherinformation map 358 may be a vegetative index map, such as vegetativeindex map 416. Another information map 358 may be an optical map, suchas optical map 417. Another information map 358 may be a weed map, suchas weed map 418. Another information map 358 may be a contamination map,such as contamination map 419. Information maps 358 may include variousother types of information maps that map various other characteristics,such as other maps 429.

The process by which one or more information maps 358 are selected canbe manual, semi-automated, or automated. The information maps 358 can bebased on data collected prior to a current operation, as indicated byblock 506. For instance, the data may be collected based on aerialimages taken during a previous year, or earlier in the current season,or at other times. The data may be based on data detected in ways otherthan using aerial images. For instance, the data may be collected duringa previous operation on the worksite, such an operation during aprevious year, or a previous operation earlier in the current season, orat other times. The machines performing those previous operations may beoutfitted with one or more sensors that generate sensor data indicativeof one or more characteristics. In other examples, and as describedabove, the information maps 358 may be predictive maps having predictivevalues. The predictive information map 358 can be generated during acurrent operation by predictive map generator 312 based on a modelgenerated by predictive model generator 310, as indicated by block 506.The predictive information map 358 can be predicted in other ways(before or during the current operation), such as based on othermeasured values. The data for the information maps 358 can be obtainedby predictive model generator 310 and predictive map generator 312 usingcommunication system 306 and stored in data store 302. The data for theinformation maps 358 can be obtained by material application system 300using a communication system in other ways as well, and this isindicated by block 507 in the flow diagram of FIG. 15.

As material application machine 100 is operating, in-situ sensors 308generate sensor data indicative of one or more in-situ data valuesindicative of one or more characteristics, as indicated by block 508.For example, nutrient sensors 374 generate sensor data indicative of oneor more in-situ nutrient values as indicated by block 509. Weed sensors372 generate sensor data indicative of one or more in-situ weed valuesas indicated by block 510. Material consumption sensors 376 generatesensor data indicative of one or more in-situ material consumptionvalues as indicated by block 511. Heading/speed sensors 325 generatesensor data indicative of one or more in-situ speed characteristicvalues as indicated by block 512. In some examples, data from in-situsensors 308 is georeferenced using position data from geographicposition sensor 304 as well as, in some examples, one or more of headingdata, travel speed data, machine latency data, sensor position andparameter data, as well as various other data.

At block 513, predictive model generator 310 controls one or more modelgenerators to generate one or more models that model the relationshipbetween mapped values and values sensed by in-situ sensors 308.

For instance, in one example, predictive model generator 310 controlsone or more of the model generators 441, 442, 443, 444, 445, 446, 448,and 449 to generate a predictive nutrient model that models therelationship between the mapped values, such as one or more of the soilproperty values, the yield values, the residue values, the constituentvalues, the seeding characteristic values, the topographiccharacteristic values, the vegetative index values, and the othercharacteristic values contained in the respective information map andthe in-situ nutrient values sensed by in-situ sensors 308. Predictivemodel generator 310 thus generates a predictive nutrient model, such aspredictive nutrient model 450, as indicated by block 514.

In another example, predictive model generator controls one or more ofthe model generators 1440, 1441, 1442, 1445, and 1449 to generate apredictive weed model that models the relationship between the mappedvalues, such as one or more of the vegetative index values, the opticalcharacteristic values, the weed values, and the other characteristicvalues contained in the respective information map and the in-situ weedvalues sensed by in-situ sensors 308. Predictive model generator 310thus generates a predictive weed model, such as predictive weed model1450, as indicated by block 515.

In another example, predictive model generator controls one or more ofthe model generators 2440, 2441, 2442, 2443, 2444, 2445, and 2449 togenerate a predictive material consumption model that models therelationship between the mapped values, such as one or more of the soilproperty values, the topographic characteristic values, the vegetativeindex values, the weed values, the contamination values, and the othercharacteristic values contained in the respective information map andthe in-situ material consumption values sensed by the in-situ sensors308. Predictive model generator 310 thus generates a predictive materialconsumption model, such as predictive material consumption model 2450,as indicated by block 516.

In another example, predictive model generator controls one or more ofthe model generators 3440 and 3449 to generate a predictive speed modelthat models the relationship between mapped values, such as values ofone or more characteristics in one or more information maps 358, and thein-situ speed characteristic values sensed by in-situ sensors 308.Predictive model generator 310 thus generates a predictive speed model,such as predictive speed model 3450, as indicated by block 517.

The relationship(s) or model(s) generated by predictive model generator310 are provided to predictive map generator 312.

Predictive map generator 312, at block 518, controls one or morepredictive map generators to generate one or more functional predictivemaps based on the relationship(s) or model(s) generated by predictivemodel generator 310 and one or more of the information maps 358.

For instance, in one example, predictive map generator 312 controlspredictive nutrient map generator 452 to generate a predictive nutrientmap, such as functional predictive nutrient map 460, that predictsnutrient values (or sensor value(s) indictive of nutrient values) atdifferent geographic locations in a worksite at which materialapplication machine 100 is operating using the predictive nutrient model450 and one or more of the information maps 358, such as one or more ofsoil property map 410, yield map 411, residue map 412, constituents map413, seeding map 414, topographic map 415, vegetative index map 416, andan other map 429. Generating a predictive nutrient map, such asfunctional predictive nutrient map 460 is indicated by block 519.

It should be noted that, in some examples, the functional predictivenutrient map 460 may include two or more different map layers. Each maplayer may represent a different data type, for instance, a functionalpredictive nutrient map 460 that provides two or more of a map layerthat provides predictive nutrient values based on soil property valuesfrom soil property map 410, a map layer that provides predictivenutrient values based on yield values from yield map 411, a map layerthat provides predictive nutrient values based on residue values fromresidue map 412, a map layer that provides predictive nutrient valuesbased on constituent values from constituents map 413, a map layer thatprovides predictive nutrient values based on seeding characteristicvalue from seeding map 414, a map layer that provides predictivenutrient values based on topographic characteristic values fromtopographic map 415, a map layer that provides predictive nutrientvalues based on vegetative index values from vegetative index map, and amap layer that provides predictive nutrient values based on othercharacteristic values from an other map 429. In some examples, thefunctional predictive nutrient map 460 may include a map layer thatprovides predictive nutrient values based on two or more of soilproperty values from soil property map 410, yield values from yield map411, residue values from residue map 412, constituent values fromconstituents map 413, seeding characteristic values from seeding map414, topographic characteristic values from topographic map 415,vegetative index values from vegetative index map 416, and othercharacteristic values from an other map 429.

In one example, predictive map generator 312 controls predictive weedmap generator 1452 to generate a predictive weed map, such as functionalpredictive weed map 1460, that predicts weed values (or sensor value(s)indictive of weed values) at different geographic locations in aworksite at which material application machine 100 is operating usingthe predictive weed model 1450 and one or more of the information maps358, such as one or more of vegetative index map 416, optical map 417,weed map 418, and an other map 429. Generating a predictive weed map,such as functional predictive weed map 1460 is indicated by block 520.

It should be noted that, in some examples, the functional predictiveweed map 1460 may include two or more different map layers. Each maplayer may represent a different data type, for instance, a functionalpredictive weed map 1460 that provides two or more of a map layer thatprovides predictive weed values based on vegetative index values fromvegetative index map 416, a map layer that provides predictive weedvalues based on optical characteristic values from optical map 417, amap layer that provides predictive weed values based on weed values fromweed map 418, and a map layer that provides predictive weed values basedon other characteristic values from an other map 429. In some examples,the functional predictive weed map 1460 may include a map layer thatprovides predictive weed values based on two or more of vegetative indexvalues from vegetative index map 416, optical characteristic values fromoptical map 417, weed values from weed map 418, and other characteristicvalues from an other map 429.

In one example, predictive map generator 312 controls predictivematerial consumption map generator 2452 to generate a predictivematerial consumption map, such as functional predictive materialconsumption map 2460, that predicts material consumption values (orsensor value(s) indictive of material consumption values) at differentgeographic locations in a worksite at which material application machine100 is operating using the predictive material consumption model 2450and one or more of the information maps 358, such as one or more of soilproperty map 410, topographic map 415, vegetative index map 416, weedmap 418, contamination map 419, and an other map 429. Generating apredictive material consumption map, such as functional predictivematerial consumption map 2460 is indicated by block 521.

It should be noted that, in some examples, the functional predictivematerial consumption map 2460 may include two or more different maplayers. Each map layer may represent a different data type, forinstance, a functional predictive material consumption map 2460 thatprovides two or more of a map layer that provides predictive materialconsumption values based on soil property values from soil property map410, a map layer that provides predictive material consumption valuesbased on topographic characteristic values from topographic map 415, amap layer that provides predictive material consumption values based onvegetative index values from vegetative index map 416, a map layer thatprovides predictive material consumption values based on weed valuesfrom weed map 418, a map layer that provides predictive materialconsumption values based on contamination values from contamination map419, and a map layer that provides predictive material consumptionvalues based on other characteristic values from an other map 429. Insome examples, the functional predictive material consumption map 2460may include a map layer that provides predictive material consumptionvalues based on two or more of soil property values from soil propertymap 410, topographic characteristic values from a topographic map 415,vegetative index values from vegetative index map 416, weed values fromweed map 418, contamination values from contamination map 419, and othercharacteristic values from an other map 429.

In one example, predictive map generator 312 controls predictive speedmap generator 3452 to generate a predictive speed map, such asfunctional predictive speed map 3460, that predicts speed characteristicvalues (or sensor value(s) indictive of speed characteristic values) atdifferent geographic locations in a worksite at which materialapplication machine 100 is operating using the predictive speed model3450 and one or more of the information maps 358. Generating apredictive speed map, such as functional predictive speed map 3460 isindicated by block 522.

It should be noted that, in some examples, the functional predictivespeed map 2460 may include two or more different map layers. Each maplayer may represent a different data type, for instance, a functionalpredictive material consumption map 2460 that provides two or more maplayers, each map layer providing predictive speed characteristic valuesbased on a respective characteristic, for instance a map layer thatprovides predictive speed characteristic values based on values of afirst characteristic from a first information map 358 and a map layerthat provides predictive speed characteristic values based on values ofa second characteristic from a second information map 358. In someexamples, the functional predictive speed map 3460 may include a maplayer that provides predictive speed characteristic values based onvalues of two or more characteristics, such as a map layer that providespredictive speed characteristic values based on values of a firstcharacteristic from a first information map 358 and values of a secondcharacteristic from a second information map 358.

At block 523, predictive map generator 312 configures the functionalpredictive map(s) (e.g., one or more of 460, 1460, 2460, and 3460) sothat the functional predictive map(s) are actionable (or consumable) bycontrol system 314. Predictive map generator 312 can provide thefunctional predictive map(s) to the control system 314 or to controlzone generator 313, or both. Some examples of the different ways inwhich the functional predictive map(s) (e.g., one or more of 460, 1460,2460, and 3460) can be configured or output are described with respectto blocks 523, 524, 525, and 526. For instance, predictive map generator312 configures one or more of the functional predictive maps so that theone or more functional predictive maps include values that can be readby control system 314, and used as the basis for generating controlsignals for one or more of the different controllable subsystems 316, asindicated by block 523.

At block 524, control zone generator 313 can divide the functionalpredictive maps into control zones based on the values on the functionalpredictive maps to generated functional predictive maps with controlzones. In one example, control zone generator 313 can divide thefunctional predictive nutrient map 460 into control zones based on thevalues on the functional predictive nutrient map 460 to generatefunctional predictive nutrient control zone map 461. In another example,control zone generator 313 can divide the functional predictive weed map1460 into control zones based on the values on the functional predictiveweed map 1460 to generate functional predictive weed control zone map1461. In another example, control zone generator 313 can divide thefunctional predictive material consumption map 2460 into control zonesbased on the values on the functional predictive material consumptionmap 2460 to generate functional predictive material consumption controlzone map 2461. In another example, control zone generator 313 can dividethe functional predictive speed map 3460 into control zones based on thevalues on the functional predictive seed map 3460 to generate functionalpredictive speed control zone map 3461.

Contiguously-geolocated values that are within a threshold value of oneanother can be grouped into a control zone. The threshold value can be adefault threshold value, or the threshold value can be set based on anoperator input, based on an input from an automated system, or based onother criteria. A size of the zones may be based on a responsiveness ofthe control system, the controllable subsystems, based on wearconsiderations, or on other criteria.

At block 525, predictive map generator 312 configures one or more of thefunctional predictive maps (e.g., one or more of 460, 1460, 2460, and3460) or one or more of the functional predictive control zone maps(e.g., one or more of 461, 1461, 2461, and 3461), or both, forpresentation to an operator or other user. When presented to an operatoror other user, the presentation of the one or more functional predictivemaps or of the one or more functional predictive control zone maps, orboth, may contain one or more of the predictive values on the one ormore functional predictive maps correlated to geographic location, thecontrol zones of the one or more functional predictive control zone mapscorrelated to geographic location, and settings values or controlparameters that are used based on the predicted values on the one ormore functional predictive maps or control zones on the one or morefunctional predictive control zone maps. The presentation can, inanother example, include more abstracted information or more detailedinformation. The presentation can also include a confidence level thatindicates an accuracy with which the predictive values on the one ormore functional predictive maps or the control zones on the one or morepredictive control zone maps, or both, conform to measured values thatmay be measured by sensors on material application machine 100 asmaterial application machine 100 operates at the worksite. Further whereinformation is presented to more than one location, an authenticationand authorization system can be provided to implement authentication andauthorization processes. For instance, there may be a hierarchy ofindividuals that are authorized to view and change maps and otherpresented information. By way of example, an on-board display device mayshow the maps in near real time locally on the machine, or the maps mayalso be generated at one or more remote locations, or both. In someexamples, each physical display device at each location may beassociated with a person or a user permission level. The user permissionlevel may be used to determine which display elements are visible on thephysical display device and which values the corresponding person maychange. As an example, a local operator of material application machine100 may be unable to see the information corresponding to the one ormore functional predictive maps or the one or more functional predictivecontrol zone maps, or both, or make any changes to machine operation. Asupervisor, such as a supervisor at a remote location, however, may beable to see the one or more functional predictive maps or the one ormore functional predictive control zone maps, or both, on the displaybut be prevented from making any changes. A manager, who may be at aseparate remote location, may be able to see all of the elements on theone or more functional predictive maps or the one or more functionalpredictive control zone maps, or both, and also be able to change theone or more functional predictive maps or the one or more functionalpredictive control zone maps, or both. In some instances, the one ormore functional predictive maps or the one or more functional predictivecontrol zone maps, or both, accessible and changeable by a managerlocated remotely may be used in machine control. This is one example ofan authorization hierarchy that may be implemented. The one or morefunctional predictive maps or the one or more functional predictivecontrol zone maps, or both, can be configured in other ways as well, asindicated by block 526.

At block 527, input from geographic position sensor 304 and otherin-situ sensors 308 are received by the control system 314.Particularly, at block 528, control system 314 detects an input from thegeographic position sensor 304 identifying a geographic location ofmaterial application machine 100. Block 529 represents receipt by thecontrol system 314 of sensor inputs indicative of trajectory or headingof material application machine 100, and block 530 represents receipt bythe control system 314 of a speed of material application machine 100.Block 531 represents receipt by the control system 314 of otherinformation from various in-situ sensors 308.

At block 532, control system 314 generates control signals to controlthe controllable subsystems 316 (or to other items) based on the one ormore functional predictive maps (e.g., one or more of 460, 1460, 2460,and 3460) or the one or more functional predictive control zone maps(e.g., one or more of 461, 1461, 2461, and 3461), or both, and the inputfrom the geographic position sensor 304 and any other in-situ sensors308. At block 534, control system 314 applies the control signals to thecontrollable subsystems 316 (or to other items). It will be appreciatedthat the particular control signals that are generated, and theparticular controllable subsystems 316 (or other items) that arecontrolled, may vary based upon one or more different things. Forexample, the control signals that are generated and the controllablesubsystems 316 (or other items) that are controlled may be based on thetype(s) of the functional predictive map(s) or the functional predictivecontrol zone map(s), or both, that is being used. Similarly, the controlsignals that are generated and the controllable subsystems 316 (or otheritems) that are controlled and the timing of the control signals can bebased on various latencies of material application machine 100 and theresponsiveness of the controllable subsystems 316 (or other items).

As an example, communication system controller 329 can provide controlsignals to control communication system 306 based on the functionalpredictive map(s) or the functional predictive control zone map(s), orboth. For instance, communication system controller can provide controlsignals to control communication system 306 to communicate thefunctional predictive map(s) or functional predictive control zonemap(s), or both, or data based thereon to other items of materialapplication system 300.

As another example, interface controller 330 can generate controlsignals to control an interface mechanism, such as an operator interfacemechanism 318 or a user interface mechanisms 364, or both, based on orindicative of the functional predictive map(s) or functional predictivecontrol zone map(s), such as to display the functional predictive map(s)or the functional predictive control zone map(s), or both.

As another example, material application controller 331 can generatecontrol signals to control one or more material application actuators340 to control application of material (e.g., the amount of materialthat is applied, whether or not material is applied, etc.) based on thefunctional predictive map(s) or the functional predictive control zonemap(s), or both.

As another example, propulsion controller 334 can generate controlsignals to control propulsion subsystem 350 to control a speedcharacteristic (e.g., a travel speed, an acceleration, a deceleration,etc.) of material application machine 100 based on the functionalpredictive map(s) or the functional predictive control zone map(s), orboth.

As another example, path planning controller 335 can generate controlsignals to control steering subsystem 352 to control a heading ofmaterial application machine 100 based on the functional predictivemap(s) or the functional predictive control zone map(s), or both.

As another example, logistics module 315 can generate logistics controlsignals to control one or more controllable subsystems 316 or variousother items of material application system 300 based on the functionalpredictive map(s) or the functional predictive control zone map(s), orboth. Logistics module 315 will be discussed in more detail in FIGS.16-17.

These are merely some examples. Control system 314 can generate any of anumber of control signals to control any of a number of items ofmaterial application system 300.

At block 536, a determination is made as to whether the operation hasbeen completed. If the operation is not completed, the processingadvances to block 538 where in-situ sensor data from geographic positionsensor 304 and in-situ sensors 308 (and perhaps other sensors) continueto be read and further generation and application of control signals canbe performed based on the inputs at block 538 and functional predictivemap(s) or the functional predictive control zone map(s), or both.

In some examples, at block 540, material application system 300 can alsodetect learning trigger criteria to perform machine learning on one ormore of the one or more functional predictive maps (e.g., one or more of460, 1460, 2460, and 3460), the one or more functional predictivecontrol zone maps (e.g., one or more of 461, 1461, 2461, and 3461), theone or more predictive models (e.g., one or more of 450, 1450, 2450, and3450), the one or more zones generated by control zone generator 313,the one or more control algorithms implemented by the controllers in thecontrol system 314, and other triggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 542, 544, 546, 548, and 549. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data are obtained from in-situ sensors 308. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 308 that exceeds a threshold trigger or causes thepredictive model generator 310 to generate a new predictive model thatis used by predictive map generator 312. Thus, as material applicationmachine 100 continues an operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 308 triggers the creationof a new relationship represented by one or more new predictive modelsgenerated by predictive model generator 310. Further, one or more newfunctional predictive maps, one or more new functional predictivecontrol zone maps, or both, can be generated using the respective one ormore new predictive models. Block 542 represents detecting a thresholdamount of in-situ sensor data used to trigger creation of one or morenew predictive models.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 308 are changing,such as over time or compared to previous values. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in the one or moreinformation maps 358) are within a selected range or is less than adefined amount, or below a threshold value, then one or more newpredictive models are not generated by the predictive model generator310. As a result, the predictive map generator 312 does not generate oneor more new functional predictive maps, one or more new functionalpredictive control zone maps, or both. However, if variations within thein-situ sensor data are outside of the selected range, are greater thanthe defined amount, or are above the threshold value, for example, thenthe predictive model generator 310 generates one or more new predictivemodels using all or a portion of the newly received in-situ sensor datathat the predictive map generator 312 uses to generate one or more newfunctional predictive maps which can be provided to control zonegenerator 313 for the creation of one or more new functional predictivecontrol zone maps. At block 544, variations in the in-situ sensor data,such as a magnitude of an amount by which the data exceeds the selectedrange or a magnitude of the variation of the relationship between thein-situ sensor data and the information in the one or more informationmaps, can be used as a trigger to cause generation of one or more of oneor more new predictive models, one or more new functional predictivemaps, and one or more new functional predictive control zone maps.Keeping with the examples described above, the threshold, the range, andthe defined amount can be set to default values; set by an operator oruser interaction through a user interface; set by an automated system;or set in other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 310 switches to a different information map(different from the originally selected information map), then switchingto the different information map may trigger re-learning by predictivemodel generator 310, predictive map generator 312, control zonegenerator 313, control system 314, or other items. In another example,transitioning of material application machine 100 to a differenttopography, a different control zone, a different region of theworksite, a different area with different grouped characteristics (suchas a different crop genotype area) may be used as learning triggercriteria as well.

In some instances, an operator 360 or user 366 can also edit thefunctional predictive map(s) or functional predictive control zonemap(s), or both. The edits can change a value on the functionalpredictive map(s), change a size, shape, position, or existence of acontrol zone on functional predictive control zone map(s), or both.Block 546 shows that edited information can be used as learning triggercriteria.

In some instances, it may also be that an operator 360 or user 366observes that automated control of a controllable subsystem, is not whatthe operator or user desires. In such instances, the operator 360 oruser 366 may provide a manual adjustment to the controllable subsystemreflecting that the operator 360 or user 366 desires the controllablesubsystem to operate in a different way than is being commanded bycontrol system 314. Thus, manual alteration of a setting by the operatoror user can cause one or more of predictive model generator 310 torelearn one or more predictive models, predictive map generator 312 togenerate one or more new functional predictive maps, control zonegenerator 313 to generate one or more new control zones on one or morefunctional predictive maps, and a control system to relearn a controlalgorithm or to perform machine learning on one or more of thecontrollers in the control system based upon the adjustment by theoperator or user, as shown in block 548. Block 549 represents the use ofother triggered learning criteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval, as indicated byblock 550.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 550,then one or more of the predictive model generator 310, predictive mapgenerator 312, control zone generator 313, control system 314 performsmachine learning to generate new predictive model(s), new functionalpredictive map(s), new control zone(s), and new control algorithm(s),respectively, based upon the learning trigger criteria. The newpredictive model(s), the new functional predictive map(s), the newcontrol zone(s), and the new control algorithm(s) are generated usingany additional data that has been collected since the last learningoperation was performed. Performing relearning is indicated by block552.

If the operation has been completed, operation moves from block 552 toblock 554 where one or more of the functional predictive map(s), thefunctional predictive control zone map(s), the predictive model(s), thecontrol zone(s), and the control algorithm(s) are stored. The functionalpredictive map(s), the functional predictive control zone map(s), thepredictive model(s), the control zone(s), and the control algorithm(s)may be stored locally on a data store of a machine or stored remotelyfor later use.

If the operation has not been completed, operation returns to block 523such that the new functional predictive map(s), the new functionalpredictive control zone map(s), the new control zone(s), and/or the newcontrol algorithm(s) can be used to control the material applicationmachine 100 or other items of material application system 300, or both.

FIG. 16 is a block diagram of a portion of agricultural materialapplication system 300 shown in FIG. 10, in more detail. Particularly,FIG. 16 shows examples of the logistics module 315 in more detail. FIG.16 also illustrates information flow among the various components shown.

As illustrated in FIG. 16, logistics module 315 receives one or morematerial consumption maps 602, one or more speed maps 604, one or morefunctional predictive maps 263, one or more information maps 358, sensordata 606, material application machine dimensional data 608, route data610, material delivery vehicle data 612, threshold data 614, and variousother data 616, such as, but not limited to, other operator or userinputs.

Material consumption maps 602 can include functional predictive materialconsumption map 2460, functional predictive material consumption controlzone map 2461, as well as other material consumption maps 603, such asother types of predictive material consumption maps or prescriptivematerial consumption maps.

Speed maps 604 can include functional predictive speed map 3460,functional predictive speed control zone map 3461, as well as otherspeed maps 605, such as other types of predictive speed maps orprescriptive speed maps.

Functional predictive maps 263, in the example illustrated in FIG. 16,can include predictive maps 264, such as functional predictive nutrientmap 460 or functional predictive weed map 1460, and predictive maps withcontrol zones 265, such as functional predictive nutrient control zonemap 461 or functional predictive weed control zone map 1461.

Information maps 358 can include any of the information maps 358discussed herein as well as various other information maps that mapvalues of various other characteristics. For example, information maps358 can also include prescriptive material application maps. Aprescriptive material application map can be used in the control ofmachine 100 at the field. A prescriptive material application mapvarious machine settings values, such as material application settings(e.g., material application rate settings) across different geographiclocations in a field of interest.

Sensor data 606 includes data generated by or derived from in-situsensors 308.

Material application machine dimensional data 608 can include dataindicative of the volumetric capacity or weight capacity of the materialcontainers of material application machine 100 (e.g., tanks 107, tanks110, tanks 112, tanks 234, tanks 255, etc.). Material applicationmachine dimensional data 608 may also include the width of a towedimplement, a spray boom, a spray nozzle coverage pattern, a spreaderthrow zone, or any other width associated with material applicationperpendicular to the direction of travel of the material applicationmachine 100.

Route data 610 can include data indicative of a planned or prescribedroute of material application machine 100 at the field, including dataindicative of the route already travelled. Route data 610 can alsoinclude data indicative of a route from a location at which a materialdelivery vehicle 379 is located to the field or to a particular locationon the field. In some examples, the route data 610 may be input orprovided by an operator or user. In some examples, route data 610 may beoutput by control system 314, such as by path planning controller 335.In some examples, the route data 610 may be in the form of aninformation map 358, such as a route map that maps a planned orprescribed route of material application machine 100 at the field.

Material delivery vehicle data 612 can include data indicative of alocation of a material delivery vehicle 379. a heading or speed, or bothof material delivery vehicle 363, as well as various other data.Material delivery vehicle 379 may have on-board sensors that providesuch data which can be provided to logistics module 315 over network359.

Threshold data 614 includes data indicative of various thresholds, suchas threshold material empty levels, as well as various other thresholds.Such threshold data can be provided by an operator or user or otherwisegenerated by control system 314.

Preferred material delivery location data 615 includes data indicativeof preferred or commanded material delivery location(s). That is,location(s) at the worksite where the material delivery vehicle 363 andthe material application machine 100 are to be located to perform amaterial delivery operation. Such locations could include, for example,headlands, ends of rows, outside of the area of the field where materialis to be applied, near a field entrance, as well as various otherlocations.

Other data 616 can include any of a wide variety of other data,including, but not limited to, various other data provided by operatoror user input.

It will be noted that the various data can be stored in a data store,such as data store 302, or a data store at a different location.

As illustrated in FIG. 16, logistics module 315 includes data capturelogic 622, material delivery location identifier logic 652, distancelogic 653, arrival time logic 654, material empty logic 655, applicationrate logic 656, speed logic 657, route planning logic 658, displayelement integration component 659, map generator 660, and can includeother items 630 as well. Data capture logic 622, itself, includes sensoraccessing logic 662, data store accessing logic 664, and can includeother items 666 as well. Data capture logic 622 captures or obtains datathat can be used by other items of logistics module 315. Sensoraccessing logic 662 can be used by logistics module 315 to obtain orotherwise access sensor data (or values indicative of the sensedvariables/characteristics) provided from in-situ sensors 308.Additionally, data store accessing logic 664 can be used by logisticsmodule 315 to obtain or access data stored on data stores, such as datastore 302 or other data stores. Upon obtaining the various data,logistics module 315 generates logistics outputs 668 which can be usedin the control of material application machine 100 or other items ofmaterial application system 300.

Material empty logic 655 illustratively identifies geographic locationsat the field, along the route of material application machine 100, atwhich one or more material containers of material application machine100 will be empty or will be empty to a threshold level. Material emptylogic 655 can determine the location at which one or more materialcontainers will be empty or empty to a threshold level based on amaterial consumption map 602, as well as various other data, such asroute data 610 and sensor data 606. For instance, material empty logic655 can identify a current fill level of one or more containers based onsensor data from in-situ sensors (e.g., fill level sensors 117, 177,178, 271, etc.), a current location of material application machine 100(e.g., as indicated by geographic position sensor 304), a currentheading of material application machine 100 (e.g., as indicated byheading/speed sensor 325) and can aggregate the material consumptionvalues (e.g., predictive material consumption values), as indicated by amaterial consumption map 602, along the route of material applicationmachine 100 (as indicated by route data 610) to identify the location atwhich one or more material containers of material application machine100 will be empty or empty to a threshold level. For instance, it may bethat it is desirable to only allow the material application machine tobecome empty to a threshold level, rather than completely empty toreduce the risk of operating over ground without applying material.

In other examples, instead of using a material consumption map 602,material empty logic 655 may utilize other types of maps. For instance,logistics module 315 may receive a functional predictive nutrientcontrol zone map 461 or a functional predictive weed control zone map1461. Map 461 or map 1461 may include various machine settings values,such as material application settings (e.g., material application ratesettings), along the route of the material application machine 100 whichcan be used (aggregated) by material empty logic 655 to identify amaterial empty location. In another example, material empty logic 655may utilize an information map 358 in the form of a prescriptivematerial application map which may include various machine settingsvalues, such as material application settings (e.g., materialapplication rate settings), along the route of the material applicationmachine 100 which can be used (aggregated) by material empty logic 655to identify a material empty location.

Material delivery location identifier logic 652 identifies geographiclocations at the field at which material is to be delivered to materialapplication machine 100. In some examples, the material deliverylocations may be predetermined locations at the field (e.g., asindicated by preferred material delivery location data 615), such as inheadlands or at an area of the field that is not used for agricultural,or an area of the field that is near a field entrance. For instance, itmay be desirable to limit compaction (or other deterioration) at thefield and thus it may be preferable to have the material applicationmachine 100 travel to a location away from the operating portion of thefield to receive material. In other examples, a material deliveryvehicle 373 may travel onto the operating portion of the field todeliver material and thus the material delivery location may be ageographic location on the field along the route of the materialapplication machine 100. In some examples, the material deliverylocation is the same as the material empty location identified bymaterial empty logic 655.

Distance logic 653 illustratively determines a distance of machine(s)away from a material delivery location. For instance, distance logic 653can determine the distance of material application machine 100 away froma material delivery location based on the current position of materialapplication machine 100 (e.g., as indicated by geographic positionsensor 304) as well as the route of material application machine 100(e.g., as indicated by route data 610). Distance logic 653 can determinethe distance of a material delivery vehicle 379 away from a materialdelivery location based on the current position of the material deliveryvehicle 379 (e.g., as indicated by material delivery vehicle data 612)as well as the route of the material delivery vehicle 379 (e.g., asindicated by route data 610).

Arrival time logic 654 illustratively identifies a time at whichmachine(s) will (or can) arrive at a material delivery location. Forinstance, arrival time logic 654 can determine the time at which thematerial application machine 100 will (or can) arrive at a materialdelivery location based on distance between the material applicationmachine 100 and the material delivery location as identified by distancelogic 653 as well as speed data of the material application machine 100,for instance speed characteristic values (e.g., predictive speedcharacteristic values) from a speed map 604, or based on current speedcharacteristic values of material application machine 100 (e.g., asindicated by heading/speed sensors 325). Arrival time logic 654 can alsoidentify a time at which a material delivery vehicle 379 will (or can)arrive at a material delivery location based on the distance between thematerial delivery vehicle 379 and the material delivery location asidentified by distance logic 653 as well as speed data of the materialdelivery vehicle 379, such as speed limits along the route of materialdelivery machine 379 (e.g., as indicated by route data 610), historicalspeed of material delivery machine 379 (e.g., as indicated by materialdelivery vehicle data 612), duration of intermediate stops along theroute of the material delivery machine 379 (e.g., stops due to on-roadtraffic control, stops due to replenishing at other locations, etc.), orcurrent speed of material delivery machine 379 (e.g., as indicated bymaterial delivery vehicle data 612).

Based on the arrival times, logistics module 315 may generate alogistics output 668 to control a material delivery machine 379 to begintraveling to the material delivery location to arrive at the same timeor within a threshold amount of time as the time that the materialapplication machine 100 will arrive. In other examples, logistics module315 may communicate with a material delivery service system 380 toschedule a material delivery based on the arrival time.

In some instances, it may be preferable to change the application rateof material application machine 100 such that material applicationmachine will become empty (at least to a threshold level) at the end ofa pass, rather than, somewhere else along the route (e.g., in the middleof the field or in the middle of a pass). In this way, materialapplication machine 100 will still apply material over the pass and willnot have to travel back over it. For instance, where material emptylogic 655 identifies a material empty location that is not at the end ofa pass, application rate logic 656 will identify an application ratesetting to adjust the application rate of material application machine100 such that material application machine will become empty at the endof a pass. It will be important to note that it may be that applicationrate logic 656 adjusts the operation rate in a pass that is prior to thepass that material empty logic 655 identifies as the pass in which thematerial application machine 100 will become empty.

In some examples, if the material application machine 100 is projectedto be within a threshold level of empty at the end of a pass or of thefield, such as 5% (which may be indicated by threshold data 614),application rate logic 656 may automatically identify an applicationrate setting to adjust the application rate of material applicationmachine 100 such that material application machine 100 will become emptyat the end of a pass or at the end of the field, regardless of thematerial empty location identified by material empty logic 655.

Where the material delivery vehicle 379 and material application machine100 will not (or cannot) arrive at a material delivery location at thesame time, or within a threshold amount of time of each other, based oncurrent conditions (e.g., machine settings), speed logic 657 mayidentify a speed setting to adjust a speed of material applicationmachine 100 or of material delivery vehicle 379, or both, such that thematerial application machine 100 and material delivery vehicle 379arrive at the material delivery location at the same time or within athreshold amount of time of each other. Reducing the speed of a machine100 may reduce wear, save on fuel costs, as well as provide variousother benefits.

Where the material delivery vehicle 379 and material application machine100 will not (or cannot) arrive at a material delivery location at thesame time, or within a threshold amount of time of each other, based oncurrent conditions (e.g., machine settings), route planning logic 658may identify a new route for material application machine 100 or formaterial delivery vehicle 379, or both, such that the materialapplication machine 100 and material delivery vehicle 379 arrive at thematerial delivery location at the same time or within a threshold amountof time of each other. In this way, downtime can be reduced.

Map generator 660 illustratively generates one or more logistics maps661. Logistics maps 661 illustratively map the field in which thematerial application operation is being performed and perhapssurrounding areas of the field. Logistics maps 661 may include a varietyof display elements (discussed below) and can be used in the control ofa material application machine 100 or a material delivery vehicle 379,or both. In some examples, a logistics map 661 may be one of the othermaps discussed herein, such as one of the functional predictive maps 263with logistics display elements integrated into the map.

Display element integration component 659 illustratively generates oneor more display elements, such as material delivery location displayelements, material empty location display elements, route displayelements, material application machine display elements, materialdelivery machine display elements, distance display elements, arrivaltime display elements, as well as various other display elements.Display element integration component 659 can integrate the one or moredisplay elements into one or more maps, such as one or more offunctional predictive maps 263 or a separate logistics map 661 generatedby map generator 661.

It will be noted that as the one or more functional predictive maps 263are updated or otherwise made new (as described above in FIG. 15), thelogistics outputs 668 generated by logistics module 315 can also beupdated or otherwise made new according to the updated (or new)functional predictive maps 263. For example, logistics module 315 may,based on the updated or new functional predictive maps 263, may generateupdated (or new) material empty locations, material delivery locations,distances, arrival times, speed outputs, route outputs, displayelements, logistics maps, etc.

The logistic outputs 668 can be used to control material applicationmachine 100 or material delivery vehicle 379, or both. The logisticsoutputs 668 can be displayed (or provided) on an interface mechanism,such as operator interface mechanism 318 or user interface mechanism364. The logistics outputs 668 can be provided to other items ofmaterial application system 300, such as to remote computing systems368, delivery vehicles 379, and/or delivery services systems 380.

FIG. 17 is a flow diagram showing one example operation of agriculturalmaterial application system 300 in controlling a material applicationoperation, such as by controlling a material application machine 100 orby controlling other items of material application system 300.

At block 702 logistics module 315 obtains one or more maps. Logisticsmodule 315 can obtain one or more material consumption maps 602 asindicated by block 704. Logistics module 315 can obtain one or morespeed maps 604, as indicated by block 706. Logistics module 315 canobtain one or more other maps, such as other functional predictive maps263 or information map(s) 358, or both, as indicated by block 709.

At block 710 various other data are obtained by logistics module 315.For example, logistics module 315 can obtain one or more of the dataitems illustrated in FIG. 16. As indicated by block 712, logisticsmodule 315 can obtain sensor data 606. As indicated by block 713,logistics module 315 can obtain material application machine dimensionaldata 608. As indicated by block 715, logistics module 315 can obtainreceiving route data 610. As indicated by block 716, logistics module315 can obtain material delivery vehicle data 612. As indicated by block717, logistics module 315 can obtain threshold data 614. As indicated byblock 718, logistics module 315 can obtain preferred material deliverylocation data 615. As indicated by block 719, logistics module 315 canobtain various other data 616.

At block 720 logistics module 315 generates one or more logisticsoutputs 668 based on the data obtained at blocks 702 and block 710. Asindicated by block 722, material empty logic 655 can generate, as alogistics output 668, a material empty location. As indicated by block724, material delivery location identifier logic 652 can generate, as alogistics output 668, a material delivery location. As indicated byblock 726, distance logic 653 can generate, as a logistics output 668,one or more distances, such as distance between the material applicationmachine 100 and a material delivery location and a distance between amaterial delivery vehicle 379 and the material delivery location. Asindicated by block 728, arrival time logic 654 can generate, as alogistics output 668, one or more arrival times, such as time at which amaterial application machine 100 will (or can) arrive at materialdelivery location and a time at which a material delivery vehicle 379will (or can) arrive at a material delivery location. As indicated byblock 730, application rate logic 656 can generate, as a logisticsoutput 668, one or more application rate settings which can be used tocontrol one or more material application actuators 340 to control anapplication rate of material. As indicated by block 732, speed logic 657can generate, as a logistics output 668, one or more speed outputs whichcan be used to control propulsion subsystem 350 or to control apropulsion subsystem of a material delivery vehicle 379, or both. Asindicated by block 734, route planning logic 658 can generate, as alogistics output 668, one or more routes which can be used to controlsteering subsystem 352 or a steering subsystem of a material deliveryvehicle 379, or both. As indicated by block 736, logistics module 315can generate, as a logistics output 668, one or more maps withintegrated display elements, the display elements generated andintegrated into the maps by display element integration component 659.For example, at block 738, the one or more maps may include one or morefunctional predictive maps 263 with display elements integrated or oneor more logistics maps 661 with display elements integrated, or both.Logistics module 315 can generate a variety of other logistics outputs,as indicated by block 740.

At block 742, control system 314 generate control signals based on theone or more logistics outputs 668. For example, as indicated by block744, control system 314 can generate control signals to control one ormore controllable subsystems 316 based on the one or more logisticsoutputs 668. As indicated by block 746, control system 314 can generatecontrol signals to control one or more interface mechanisms (e.g., 318or 364) to generate displays, alerts, notifications, recommendations, aswell as various other indications based on the one or more logisticsoutputs 668. As indicated by block 748, control system 314 can generatevarious other control signals based on the logistics outputs 668, suchas to communicate information to other items of material applicationsystem 300 or to control other items of material application system 300.

At block 750 it is determined if the material application operation iscomplete. If the material application operation has not been completed,operation returns to block 702. If the material application operationhas been completed, then the operation ends.

The examples herein describe the generation of a predictive model and,in some examples, the generation of a functional predictive map based onthe predictive model. The examples described herein are distinguishedfrom other approaches by the use of a model which is at least one ofmulti-variate or site-specific (i.e., georeferenced, such as map-based).Furthermore, the model is revised as the work machine is performing anoperation and while additional in-situ sensor data is collected. Themodel may also be applied in the future beyond the current worksite. Forexample, the model may form a baseline (e.g., starting point) for asubsequent operation at a different worksite or the same worksite at afuture time.

The revision of the model in response to new data may employ machinelearning methods. Without limitation, machine learning methods mayinclude memory networks, Bayes systems, decisions trees, Eigenvectors,Eigenvalues and Machine Learning, Evolutionary and Genetic Algorithms,Cluster Analysis, Expert Systems/Rules, Support Vector Machines,Engines/Symbolic Reasoning, Generative Adversarial Networks (GANs),Graph Analytics and ML, Linear Regression, Logistic Regression, LSTMsand Recurrent Neural Networks (RNNSs), Convolutional Neural Networks(CNNs), MCMC, Random Forests, Reinforcement Learning or Reward-basedmachine learning. Learning may be supervised or unsupervised.

Model implementations may be mathematical, making use of mathematicalequations, empirical correlations, statistics, tables, matrices, and thelike. Other model implementations may rely more on symbols, knowledgebases, and logic such as rule-based systems. Some implementations arehybrid, utilizing both mathematics and logic. Some models mayincorporate random, non-deterministic, or unpredictable elements. Somemodel implementations may make uses of networks of data values such asneural networks. These are just some examples of models.

The predictive paradigm examples described herein differ fromnon-predictive approaches where an actuator or other machine parameteris fixed at the time the machine, system, or component is designed, setonce before the machine enters the worksite, is reactively adjustedmanually based on operator perception, or is reactively adjusted basedon a sensor value.

The functional predictive map examples described herein also differ fromother map-based approaches. In some examples of these other approaches,an a priori control map is used without any modification based onin-situ sensor data or else a difference determined between data from anin-situ sensor and a predictive map are used to calibrate the in-situsensor. In some examples of the other approaches, sensor data may bemathematically combined with a priori data to generate control signals,but in a location-agnostic way; that is, an adjustment to an a priori,georeferenced predictive setting is applied independent of the locationof the work machine at the worksite. The continued use or end of use ofthe adjustment, in the other approaches, is not dependent on the workmachine being in a particular defined location or region within theworksite.

In examples described herein, the functional predictive maps andpredictive actuator control rely on obtained maps and in-situ data thatare used to generate predictive models. The predictive models are thenrevised during the operation to generate revised functional predictivemaps and revised actuator control. In some examples, the actuatorcontrol is provided based on functional predictive control zone mapswhich are also revised during the operation at the worksite. In someexamples, the revisions (e.g., adjustments, calibrations, etc.) are tiedto regions or zones of the worksite rather than to the whole worksite orsome non-georeferenced condition. For example, the adjustments areapplied to one or more areas of a worksite to which an adjustment isdetermined to be relevant (e.g., such as by satisfying one or moreconditions which may result in application of an adjustment to one ormore locations while not applying the adjustment to one or more otherlocations), as opposed to applying a change in a blanket way to everylocation in a non-selective way.

In some examples described herein, the models determine and apply thoseadjustments to selective portions or zones of the worksite based on aset of a priori data, which, in some instances, is multivariate innature. For example, adjustments may, without limitation, be tied todefined portions of the worksite based on site-specific factors such astopography, soil type, crop variety, soil moisture, as well as variousother factors, alone or in combination. Consequently, the adjustmentsare applied to the portions of the field in which the site-specificfactors satisfy one or more criteria and not to other portions of thefield where those site-specific factors do not satisfy the one or morecriteria. Thus, in some examples described herein, the model generates arevised functional predictive map for at least the current location orzone, the unworked part of the worksite, or the whole worksite.

As an example, in which the adjustment is applied only to certain areasof the field, consider the following. The system may determine that adetected in-situ characteristic value varies from a predictive value ofthe characteristic, such as by a threshold amount. This deviation mayonly be detected in areas of the field where the elevation of theworksite is above a certain level. Thus, the revision to the predictivevalue is only applied to other areas of the worksite having elevationabove the certain level. In this simpler example, the predictivecharacteristic value and elevation at the point the deviation occurredand the detected characteristic value and elevation at the point thedeviation cross the threshold are used to generate a linear equation.The linear equation is used to adjust the predictive characteristicvalue in areas of the worksite (which have not yet been operated on inthe current operation, such as areas where material has not yet beenapplied in the current operation) in the functional predictive map as afunction of elevation and the predicted characteristic value. Thisresults in a revised functional predictive map in which some values areadjusted while others remain unchanged based on selected criteria, e.g.,elevation as well as threshold deviation. The revised functional map isthen used to generate a revised functional control zone map forcontrolling the machine.

As an example, without limitation, consider an instance of the paradigmdescribed herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of asoil property map, a yield map, a residue map, a constituents map, aseeding map, a topographic map, a vegetative index map, and another typeof map.

In-situ sensors generate sensor data indicative of in-situcharacteristic values, such as in-situ nutrient values

A predictive model generator generates one or more predictive modelsbased on the one or more obtained maps and the in-situ sensor data, suchas a predictive nutrient model.

A predictive map generator generates one or more functional predictivemaps based on a model generated by the predictive model generator andthe one or more obtained maps. For example, the predictive map generatormay generate a functional predictive nutrient map that maps predictivenutrient values to one or more locations on the worksite based on apredictive nutrient model and the one or more obtained maps.

Control zones, which include machine settings values, can beincorporated into the functional predictive nutrient map to generate afunctional predictive nutrient map with control zones.

As another example, without limitation, consider an instance of theparadigm described herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of avegetative index map, an optical map, a weed map, and another type ofmap.

In-situ sensors generate sensor data indicative of in-situcharacteristic values, such as in-situ weed values

A predictive model generator generates one or more predictive modelsbased on the one or more obtained maps and the in-situ sensor data, suchas a predictive weed model.

A predictive map generator generates one or more functional predictivemaps based on a model generated by the predictive model generator andthe one or more obtained maps. For example, the predictive map generatormay generate a functional predictive weed map that maps predictive weedvalues to one or more locations on the worksite based on a predictiveweed model and the one or more obtained maps.

Control zones, which include machine settings values, can beincorporated into the functional predictive weed map to generate afunctional predictive weed map with control zones.

As another example, without limitation, consider an instance of theparadigm described herein which is parameterized as follows.

One or more maps of the field are obtained, such as one or more of asoil property map, a topographic map, a vegetative index map, a weedmap, a contamination map, and another type of map.

In-situ sensors generate sensor data indicative of in-situcharacteristic values, such as in-situ material consumption values

A predictive model generator generates one or more predictive modelsbased on the one or more obtained maps and the in-situ sensor data, suchas a predictive material consumption model.

A predictive map generator generates one or more functional predictivemaps based on a model generated by the predictive model generator andthe one or more obtained maps. For example, the predictive map generatormay generate a functional predictive material consumption map that mapspredictive material consumption values to one or more locations on theworksite based on a predictive material consumption model and the one ormore obtained maps.

Control zones, which include machine settings values, can beincorporated into the functional predictive material consumption map togenerate a functional predictive material consumption map with controlzones.

As another example, without limitation, consider an instance of theparadigm described herein which is parameterized as follows.

One or more maps of the field are obtained.

In-situ sensors generate sensor data indicative of in-situcharacteristic values, such as in-situ speed characteristic values

A predictive model generator generates one or more predictive modelsbased on the one or more obtained maps and the in-situ sensor data, suchas a predictive speed model.

A predictive map generator generates one or more functional predictivemaps based on a model generated by the predictive model generator andthe one or more obtained maps. For example, the predictive map generatormay generate a functional predictive speed map that maps predictivespeed characteristic values to one or more locations on the worksitebased on a predictive speed model and the one or more obtained maps.

Control zones, which include machine settings values, can beincorporated into the functional predictive speed map to generate afunctional predictive speed map with control zones.

As the mobile machine continues to operate at the worksite, additionalin-situ sensor data is collected. A learning trigger criteria can bedetected, such as threshold amount of additional in-situ sensor databeing collected, a magnitude of change in a relationship (e.g., thein-situ characteristic values varies to a certain [e.g., threshold]degree from a predictive value of the characteristic), and operator oruser makes edits to the predictive map(s) or to a control algorithm, orboth, a certain (e.g., threshold) amount of time elapses, as well asvarious other learning trigger criteria. The predictive model(s) arethen revised based on the additional in-situ sensor data and the valuesfrom the obtained maps. The functional predictive maps or the functionalpredictive control zone maps, or both, are then revised based on therevised model(s) and the values in the obtained maps.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. They arefunctional parts of the systems or devices to which they belong and areactivated by and facilitate the functionality of the other components oritems in those systems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, they can be actuated using operator interface mechanisms suchas a point and click device, such as a track ball or mouse, hardwarebuttons, switches, a joystick or keyboard, thumb switches or thumb pads,etc., a virtual keyboard or other virtual actuators. In addition, wherethe screen on which the user actuatable operator interface mechanismsare displayed is a touch sensitive screen, the user actuatable operatorinterface mechanisms can be actuated using touch gestures. Also, useractuatable operator interface mechanisms can be actuated using speechcommands using speech recognition functionality. Speech recognition maybe implemented using a speech detection device, such as a microphone,and software that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, some of which are describedbelow, that perform the functions associated with those systems,components, or logic, or interactions. In addition, any or all of thesystems, components, logic and interactions may be implemented bysoftware that is loaded into a memory and is subsequently executed by aprocessor or server or other computing component, as described below.Any or all of the systems, components, logic and interactions may alsobe implemented by different combinations of hardware, software,firmware, etc., some examples of which are described below. These aresome examples of different structures that may be used to implement anyor all of the systems, components, logic and interactions describedabove. Other structures may be used as well.

FIG. 18 is a block diagram of a mobile agricultural material applicationmachine 1000, which may be similar to mobile material applicationmachine 100 shown in FIG. 10. The mobile material application machine1000 communicates with elements in a remote server architecture 900. Insome examples, remote server architecture 900 provides computation,software, data access, and storage services that do not require end-userknowledge of the physical location or configuration of the system thatdelivers the services. In various examples, remote servers may deliverthe services over a wide area network, such as the internet, usingappropriate protocols. For instance, remote servers may deliverapplications over a wide area network and may be accessible through aweb browser or any other computing component. Software or componentsshown in FIG. 10 as well as data associated therewith, may be stored onservers at a remote location. The computing resources in a remote serverenvironment may be consolidated at a remote data center location, or thecomputing resources may be dispersed to a plurality of remote datacenters. Remote server infrastructures may deliver services throughshared data centers, even though the services appear as a single pointof access for the user. Thus, the components and functions describedherein may be provided from a remote server at a remote location using aremote server architecture. Alternatively, the components and functionsmay be provided from a server, or the components and functions can beinstalled on client devices directly, or in other ways.

In the example shown in FIG. 18, some items are similar to those shownin FIG. 10 and those items are similarly numbered. FIG. 18 specificallyshows that predictive model generator 310, predictive map generator 312,and logistics module 315 may be located at a server location 902 that isremote from the material application machine 1000. Therefore, in theexample shown in FIG. 18, material application machine 1000 accessessystems through remote server location 902. In other examples, variousother items may also be located at server location 902, such aspredictive model 311, functional predictive maps 263 (includingpredictive maps 264 and predictive control zone maps 265), control zonegenerator 313, and processing system 338.

FIG. 18 also depicts another example of a remote server architecture.FIG. 18 shows that some elements of FIG. 10 may be disposed at a remoteserver location 902 while others may be located elsewhere. By way ofexample, data store 302 may be disposed at a location separate fromlocation 902 and accessed via the remote server at location 902.Regardless of where the elements are located, the elements can beaccessed directly by material application machine 1000 through a networksuch as a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated or manual information collectionsystem. As the mobile machine 1000 comes close to the machine containingthe information collection system, such as a fuel truck prior tofueling, the information collection system collects the information fromthe mobile machine 1000 using any type of ad-hoc wireless connection.The collected information may then be forwarded to another network whenthe machine containing the received information reaches a location wherewireless telecommunication service coverage or other wireless coverageis available. For instance, a fuel truck may enter an area havingwireless communication coverage when traveling to a location to fuelother machines or when at a main fuel storage location. All of thesearchitectures are contemplated herein. Further, the information may bestored on the material application machine 1000 until the materialapplication machine 1000 enters an area having wireless communicationcoverage. The material application machine 1000, itself, may send theinformation to another network.

It will also be noted that the elements of FIG. 10, or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 902 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 19 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's handheld device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of material application machine 100, for use ingenerating, processing, or displaying the maps discussed above. FIGS.20-21 are examples of handheld or mobile devices.

FIG. 19 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 10, that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 20 shows one example in which device 16 is a tablet computer 1100.In FIG. 20, computer 1100 is shown with user interface display screen1102. Screen 1102 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 1100 may also usean on-screen virtual keyboard. Of course, computer 1100 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 1100 may also illustratively receive voice inputs as well.

FIG. 21 is similar to FIG. 20 except that the device is a smart phone71. Smart phone 71 has a touch sensitive display 73 that displays iconsor tiles or other user input mechanisms 75. Mechanisms 75 can be used bya user to run applications, make calls, perform data transferoperations, etc. In general, smart phone 71 is built on a mobileoperating system and offers more advanced computing capability andconnectivity than a feature phone.

Note that other forms of the devices 16 are possible.

FIG. 22 is one example of a computing environment in which elements ofFIG. 10 can be deployed. With reference to FIG. 22, an example systemfor implementing some embodiments includes a computing device in theform of a computer 1210 programmed to operate as discussed above.Components of computer 1210 may include, but are not limited to, aprocessing unit 1220 (which can comprise processors or servers fromprevious FIGS.), a system memory 1230, and a system bus 1221 thatcouples various system components including the system memory to theprocessing unit 1220. The system bus 1221 may be any of several types ofbus structures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Memoryand programs described with respect to FIG. 10 can be deployed incorresponding portions of FIG. 22.

Computer 1210 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 1210 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 1210. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 1230 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 1231 and random access memory (RAM) 1232. A basic input/outputsystem 1233 (BIOS), containing the basic routines that help to transferinformation between elements within computer 1210, such as duringstart-up, is typically stored in ROM 1231. RAM 1232 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 1220. By way ofexample, and not limitation, FIG. 22 illustrates operating system 1234,application programs 1235, other program modules 1236, and program data1237.

The computer 1210 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 22 illustrates a hard disk drive 1241 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 1255,and nonvolatile optical disk 1256. The hard disk drive 1241 is typicallyconnected to the system bus 1221 through a non-removable memoryinterface such as interface 1240, and optical disk drive 1255 aretypically connected to the system bus 1221 by a removable memoryinterface, such as interface 1250.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 22, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 1210. In FIG. 22, for example, hard disk drive 1241 isillustrated as storing operating system 1244, application programs 1245,other program modules 1246, and program data 1247. Note that thesecomponents can either be the same as or different from operating system1234, application programs 1235, other program modules 1236, and programdata 1237.

A user may enter commands and information into the computer 1210 throughinput devices such as a keyboard 1262, a microphone 1263, and a pointingdevice 1261, such as a mouse, trackball or touch pad. Other inputdevices (not shown) may include a joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 1220 through a user input interface 1260 that iscoupled to the system bus, but may be connected by other interface andbus structures. A visual display 1291 or other type of display device isalso connected to the system bus 1221 via an interface, such as a videointerface 1290. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 1297 and printer 1296,which may be connected through an output peripheral interface 1295.

The computer 1210 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 1280.

When used in a LAN networking environment, the computer 1210 isconnected to the LAN 1271 through a network interface or adapter 1270.When used in a WAN networking environment, the computer 1210 typicallyincludes a modem 1272 or other means for establishing communicationsover the WAN 1273, such as the Internet. In a networked environment,program modules may be stored in a remote memory storage device. FIG. 22illustrates, for example, that remote application programs 1285 canreside on remote computer 1280.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of the claims.

What is claimed is:
 1. An agricultural material application systemcomprising: a geographic position sensor that detects a geographiclocation of a mobile material application machine at a field; a controlsystem that: receives a predictive map that maps predictive weed valuesto different geographic locations in the field; and generates a controlsignal to control a controllable subsystem of the mobile materialapplication machine based on the geographic location of the mobilematerial application machine and the predictive map.
 2. The agriculturalmaterial application system of claim 1 and further comprising: anin-situ sensor that detects a weed value corresponding to a geographiclocation; a predictive model generator that: receives an information mapthat maps values of a characteristic corresponding to differentgeographic locations in the field; and generates a predictive modelindicative of a relationship between values of the characteristic andweed values based on the weed value detected by the in-situ sensorcorresponding to the geographic location and a value of thecharacteristic in the information map corresponding to the geographiclocation; and a predictive map generator that generates, as thepredictive map, a functional predictive map of the field that mapspredictive weed values to the different geographic locations in thefield based on the values of the characteristic in the information mapand based on the predictive model.
 3. The agricultural materialapplication system of claim 2 wherein the weed value is indicative ofone or more of weed presence, weed type, weed size, and weed intensity.4. The agricultural material application system of claim 2, wherein theinformation map comprises a vegetative index map that maps vegetativeindex values to the different geographic locations in the field; whereinthe predictive model generator generates, as the predictive model, apredictive weed model that models a relationship between vegetativeindex values and weed values based on the weed value detected by thein-situ sensor corresponding to the geographic location and a vegetativeindex value in the vegetative index map at the geographic location towhich the detected weed values corresponds, the predictive weed modelbeing configured to receive a vegetative index value as a model inputand generate a predictive weed value as a model output; and wherein thepredictive map generator generates, as the functional predictive map, afunctional predictive weed map that maps predictive weed values to thedifferent geographic locations in the field based on the vegetativeindex values in the information map and based on the predictive weedmodel.
 5. The agricultural material application system of claim 2,wherein the information map comprises an optical map that maps opticalcharacteristic values to the different geographic locations in thefield; wherein the predictive model generator generates, as thepredictive model, a predictive weed model that models a relationshipbetween optical characteristic values and weed values based on the weedvalue detected by the in-situ sensor corresponding to the geographiclocation and an optical characteristic value in the optical map at thegeographic location to which the detected weed values corresponds, thepredictive weed model being configured to receive an opticalcharacteristic value as a model input and generate a predictive weedvalue as a model output; and wherein the predictive map generatorgenerates, as the functional predictive map, a functional predictiveweed map that maps predictive weed values to the different geographiclocations in the field based on the optical characteristic values in theinformation map and based on the predictive weed model.
 6. Theagricultural material application system of claim 2, wherein theinformation map comprises a weed map that maps weed values to thedifferent geographic locations in the field; wherein the predictivemodel generator generates, as the predictive model, a predictive weedmodel that models a relationship between weed values and weed valuesbased on the weed value detected by the in-situ sensor corresponding tothe geographic location and a weed value in the weed map at thegeographic location to which the detected weed values corresponds, thepredictive weed model being configured to receive a weed value as amodel input and generate a predictive weed value as a model output; andwherein the predictive map generator generates, as the functionalpredictive map, a functional predictive weed map that maps predictiveweed values to the different geographic locations in the field based onthe weed values in the information map and based on the predictive weedmodel.
 7. The agricultural material application system of claim 1,wherein the information map comprises two or more information maps, eachof the two or more information maps mapping values of a respectivecharacteristic to the different geographic locations in the field;wherein the predictive model generator generates, as the predictivemodel, a predictive weed model indicative of a relationship betweenvalues of the two or more respective characteristics and weed valuesbased on the weed value detected by the in-situ sensor corresponding tothe geographic location and values of the two or more respectivecharacteristics in the two or more information maps corresponding to thegeographic location, the predictive weed model being configured toreceives a value of each of the two or more respective characteristicsas model inputs and generate a predictive weed value as a model output;and wherein the predictive map generator generates, as the functionalpredictive map, a functional predictive weed map that maps predictiveweed values to the different geographic locations in the field based onthe values of the two more respective characteristics in the two or moreinformation maps and the predictive weed model.
 8. The agriculturalmaterial application system of claim 7, wherein the controllablesubsystem comprises a material application actuator and wherein thecontrol signal controls the material application actuator to increase anamount of material applied by the material application machine based onthe functional predictive weed map.
 9. The agricultural materialapplication system of claim 7, wherein the controllable subsystemcomprises a material application actuator and wherein the control signalcontrols the material application actuator to decrease an amount ofmaterial applied by the material application machine based on thefunctional predictive weed map.
 10. The agricultural materialapplication system of claim 7, wherein the controllable subsystemcomprises a material application actuator and wherein the control signalcontrols the material application actuator to deactivate or activate acomponent of the material application machine based on the functionalpredictive weed map.
 11. A method of controlling a mobile agriculturalmaterial application machine comprising: receiving a predictive map of afield that maps predictive weed values corresponding to differentgeographic locations in the field; detecting a geographic location ofthe mobile agricultural material application machine at the field;controlling a controllable subsystem of the mobile agricultural materialapplication machine based on the geographic location of the mobileagricultural material application machine and the predictive map. 12.The method of claim 11 and further comprising: receiving an informationmap that maps values of a characteristic to different geographiclocations in a field; obtaining in-situ sensor data indicative of a weedvalue corresponding to a geographic location at the field; generating apredictive weed model indicative of a relationship between values of thecharacteristic and weed values; and generating, as the predictive map, afunctional predictive weed map of the field, that maps predictive weedvalues to the different geographic locations in the field based on thevalues of the characteristic in the information map and the predictivemodel.
 13. The method of claim 12, wherein controlling a controllablesubsystem comprises controlling a material application actuator of themobile agricultural material application machine based on the geographiclocation of the mobile agricultural material application machine and thefunctional predictive weed map.
 14. The method of claim 13, whereincontrolling the material application actuator comprises controlling thematerial application actuator of the mobile agricultural materialapplication machine to adjust a rate at which material is applied to thefield based on the geographic location of the mobile agriculturalmaterial application machine and the functional predictive weed map. 15.The method of claim 13, wherein controlling the material applicationactuator comprises controlling the material application actuator of themobile agricultural material application machine to activate ordeactivate a component of the mobile agricultural material applicationmachine based on the geographic location of the mobile agriculturalmaterial application machine and the functional predictive weed map. 16.A mobile agricultural material application machine, comprising: ageographic position sensor that detects a geographic location of themobile agricultural material application machine at a field; and acontrol system that: receives a predictive map that maps predictive weedvalues to different geographic locations in the field; and generates acontrol signal based on the geographic location of the mobileagricultural material application machine at the field and thepredictive map.
 17. The mobile agricultural material application machineof claim 16 and further comprising: a communication system that receivesan information map that maps values of a characteristic to differentgeographic locations in the field; an in-situ sensor that detects a weedvalue corresponding to the geographic location; a predictive modelgenerator that generates a predictive weed model indicative of arelationship between values of the characteristic and weed values basedon the weed value detected by the in-situ sensor corresponding to thegeographic location and a value of the characteristic in the informationmap at the geographic location; and a predictive map generator thatgenerates, as the predictive map, a functional predictive weed map ofthe field that maps predictive weed values to the different geographiclocations in the field based on the values of the characteristic in theinformation map at those different geographic locations and based on thepredictive weed model.
 18. The mobile agricultural machine of claim 17,wherein the control system generates the control signal to control acontrollable subsystem of the mobile agricultural material applicationmachine.
 19. The mobile agricultural material application machine ofclaim 17, wherein the control system generates the control signal tocontrol an actuator that is controllably actuatable to adjust a rate atwhich material is applied to the field.
 20. The mobile agriculturalmaterial application machine of claim 17, wherein the control systemgenerates the control signal to control an actuator to activate ordeactivate a component of the mobile agricultural material applicationmachine.